diff --git a/.env.example b/.env.example
index 0ec4b10..5adfdaa 100644
--- a/.env.example
+++ b/.env.example
@@ -1,8 +1,12 @@
-TEST_MODE=false
-STABILITY=dev
+TEST_MODE=False 
+DEV_MODE=True 
 
-#DB Config
-sqlHost=""
-sqlPort=
-sqlPassword=""
-sqlSchema=""
\ No newline at end of file
+#Database Config 
+DB_HOST=
+DB_PORT=
+DB_PW=
+DB_SCHEMA=
+
+#User Config
+APP_SECRET=
+VERIFICATION_SECRET=
\ No newline at end of file
diff --git a/app/database/job_db.py b/app/database/job_db.py
index 0c0a54d..d07680a 100644
--- a/app/database/job_db.py
+++ b/app/database/job_db.py
@@ -1,7 +1,7 @@
 """
 Datenbank-Komponente für Jobs
 """
-from sqlalchemy import Column, String, Integer, DateTime, Enum, Text
+from sqlalchemy import Column, String, Integer, DateTime, Enum, JSON
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.orm import declarative_base, sessionmaker
 
@@ -18,12 +18,12 @@ class DBJob(Base):
     __tablename__ = "jobs"
 
     id = Column(Integer, primary_key=True, index=True)
-    user_id = Column(Integer, nullable=False)
+    user_id = Column(String(255), nullable=False)
     created_at = Column(DateTime, nullable=False)
     job_name = Column(String(255), nullable=True)
     status = Column(Enum(JobStatus), nullable=False)
     job_parameters = Column(String(5000), nullable=False)
-    json_values = Column(Text, nullable=True)
+    json_values = Column(JSON, nullable=True)
 
 
 engine = get_async_engine()
diff --git a/app/models/basic_kmeans_model.py b/app/models/basic_kmeans_model.py
index 6c39a7a..d305d7f 100644
--- a/app/models/basic_kmeans_model.py
+++ b/app/models/basic_kmeans_model.py
@@ -93,32 +93,3 @@ class KMeansResult3D(BasicKMeansResult):
     """
     z_label: str
     cluster: List[Cluster3D]
-
-
-class KMeansResultND(BaseModel):
-    """
-    Model representing the result of the KMeans clustering process in n dimensions.
-
-    Attributes:
-    - user_id (int): User ID.
-    - request_id (int): Request ID.
-    - clusters (List[Cluster]): List of resulting clusters.
-    - x_label (str): Label for the X-coordinate.
-    - y_label (str): Label for the Y-coordinate.
-    - iterations (int): Number of iterations the algorithm ran.
-    - used_distance_metric (str): The distance metric used for clustering.
-    - filename (str): Name of the file containing the data points.
-    - k_value (int): Number of clusters used.
-    - important_features (Dict[str, float]): Dictionary of: 
-                            important features with their contributions.
-    """
-    user_id: int
-    request_id: int
-    clusters: List[Cluster]
-    x_label: str
-    y_label: str
-    iterations: int
-    used_distance_metric: str
-    name: str
-    k_value: int
-    important_features: Dict[str, float]
diff --git a/app/models/job_model.py b/app/models/job_model.py
index 3847cfa..73036a3 100644
--- a/app/models/job_model.py
+++ b/app/models/job_model.py
@@ -23,7 +23,7 @@ class JobResponse(BaseModel):
     Modell für Rückgabe aus Job-Endpunkt
     """
     job_id: int
-    user_id: int
+    user_id: str
     job_name: str
     created_at: str
     status: JobStatus
@@ -36,6 +36,7 @@ class JobResponseFull(JobResponse):
     """
     json_values: str
 
+
 # pylint: disable=too-few-public-methods
 class UserJob:
     """
@@ -53,8 +54,6 @@ class Type(Enum):
     def __init__(self, jobtype, parameters):
         """
         Konstruktur
-        :param jobtype:
-        :param parameters:
         """
         self.jobtype = jobtype
         self.parameters = parameters
@@ -62,7 +61,6 @@ def __init__(self, jobtype, parameters):
     def to_json(self):
         """
         Klasse als JSON
-        :return:
         """
         return dumps(self, default=lambda o: o.__dict__,
                      sort_keys=False, indent=None)
diff --git a/app/routers/advanced_kmeans_router.py b/app/routers/advanced_kmeans_router.py
index d6b5a91..d27d2fe 100644
--- a/app/routers/advanced_kmeans_router.py
+++ b/app/routers/advanced_kmeans_router.py
@@ -25,7 +25,8 @@ async def advanced_kmeans(
     kmeans_type: str = Query("OptimizedKMeans",
                              description="OptimizedKMeans/OptimizedMiniBatchKMeans"),
     user_id: int = Query(0, description="User ID"),
-    request_id: int = Query(0, description="Request ID")
+    request_id: int = Query(0, description="Request ID"),
+    normalize: bool = True
 ):
     """
     Endpoint for KMeans clustering with automatic k determination.
@@ -49,7 +50,8 @@ async def advanced_kmeans(
             kmeans_type,
             user_id,
             request_id,
-            selected_columns=[column1, column2]
+            selected_columns=[column1, column2],
+            normalize= normalize
         )
         # Return the KMeansResult object.
         return kmeans_result
diff --git a/app/routers/advanced_n_d_kmeans_router.py b/app/routers/advanced_n_d_kmeans_router.py
index e3f0416..3f443ea 100644
--- a/app/routers/advanced_n_d_kmeans_router.py
+++ b/app/routers/advanced_n_d_kmeans_router.py
@@ -22,7 +22,8 @@ async def advanced_kmeans_nd(
     kmeans_type: str = Query("OptimizedKMeans",
                              description="OptimizedKMeans/OptimizedMiniBatchKMeans"),
     user_id: int = Query(0, description="User ID"),
-    request_id: int = Query(0, description="Request ID")
+    request_id: int = Query(0, description="Request ID"),
+    use_3d_model:bool = False
 ):
     """
     Endpoint for advanced N-D KMeans clustering 
@@ -44,7 +45,8 @@ async def advanced_kmeans_nd(
             distance_metric=distance_metric,
             kmeans_type=kmeans_type,
             user_id=user_id,
-            request_id=request_id
+            request_id=request_id,
+            use_3d_model=use_3d_model
         )
         # Return the KMeansResultND object.
         return advanced_kmeans_result_nd
diff --git a/app/routers/advanced_three_d_kmeans_router.py b/app/routers/advanced_three_d_kmeans_router.py
index 8e834a3..12ed86a 100644
--- a/app/routers/advanced_three_d_kmeans_router.py
+++ b/app/routers/advanced_three_d_kmeans_router.py
@@ -16,11 +16,11 @@
 # pylint: disable=duplicate-code
 async def advanced_kmeans_3d(
     file: UploadFile = File(...),
-    column1: int = Query(0, alias="Column 1",
+    column1: int = Query(0,
                           description="Index of the first column"),
-    column2: int = Query(1, alias="Column 2",
+    column2: int = Query(1,
                           description="Index of the second column"),
-    column3: int = Query(2, alias="Column 3",
+    column3: int = Query(2,
                           description="Index of the third column"),
     distance_metric: str = Query(
             "EUCLIDEAN",
@@ -28,7 +28,8 @@ async def advanced_kmeans_3d(
     kmeans_type: str = Query("OptimizedKMeans",
                              description="OptimizedKMeans/OptimizedMiniBatchKMeans"),
     user_id: int = Query(0, description="User ID"),
-    request_id: int = Query(0, description="Request ID")
+    request_id: int = Query(0, description="Request ID"),
+    normalize: bool = True
 ):
     """
     Endpoint for advanced 3D KMeans clustering with automatic k determination.
@@ -53,7 +54,8 @@ async def advanced_kmeans_3d(
             kmeans_type=kmeans_type,
             user_id=user_id,
             request_id=request_id,
-            selected_columns=[column1, column2, column3]
+            selected_columns=[column1, column2, column3],
+            normalize=normalize
         )
         # Return the KMeansResult3D object.
         return kmeans_result_3d
diff --git a/app/routers/basic_kmeans_router.py b/app/routers/basic_kmeans_router.py
index e97b3ee..d0ccafc 100644
--- a/app/routers/basic_kmeans_router.py
+++ b/app/routers/basic_kmeans_router.py
@@ -25,9 +25,10 @@ async def kmeans(
             description="/".join(BaseOptimizedKMeans.supported_distance_metrics.keys())),
     kmeans_type: str = Query("OptimizedKMeans",
                              description="OptimizedKMeans/OptimizedMiniBatchKMeans"),
-    k_clusters: int = Query(2, alias="kCluster", description="Number of clusters"),
+    k_clusters: int = Query(2, description="Number of clusters"),
     user_id: int = Query(0, description="User ID"),
-    request_id: int = Query(0, description="Request ID")
+    request_id: int = Query(0, description="Request ID"),
+    normalize: bool = True
 ):
     """
     Endpoint for KMeans clustering.
@@ -53,7 +54,8 @@ async def kmeans(
             kmeans_type=kmeans_type,
             user_id=user_id,
             request_id=request_id,
-            selected_columns=[column1, column2]
+            selected_columns=[column1, column2],
+            normalize=normalize
         )
         # Return the KMeansResult object.
         return kmeans_result
diff --git a/app/routers/basic_n_d_kmeans_router.py b/app/routers/basic_n_d_kmeans_router.py
index 20470e6..20c6084 100644
--- a/app/routers/basic_n_d_kmeans_router.py
+++ b/app/routers/basic_n_d_kmeans_router.py
@@ -22,7 +22,8 @@ async def kmeans_nd(
                              description="OptimizedKMeans/OptimizedMiniBatchKMeans"),
     k_clusters: int = Query(2, description="Number of clusters"),
     user_id: int = Query(0, description="User ID"),
-    request_id: int = Query(0, description="Request ID")
+    request_id: int = Query(0, description="Request ID"),
+    use_3d_model:bool=False
 ):
     """
     Endpoint for N-D KMeans clustering with dimensionality reduction to 2D.
@@ -45,7 +46,8 @@ async def kmeans_nd(
             distance_metric=distance_metric,
             kmeans_type=kmeans_type,
             user_id=user_id,
-            request_id=request_id
+            request_id=request_id,
+            use_3d_model=use_3d_model
         )
         # Return the KMeansResultND object.
         return kmeans_result_nd
diff --git a/app/routers/basic_three_d_kmeans_router.py b/app/routers/basic_three_d_kmeans_router.py
index 5b09d07..3860f77 100644
--- a/app/routers/basic_three_d_kmeans_router.py
+++ b/app/routers/basic_three_d_kmeans_router.py
@@ -27,7 +27,8 @@ async def kmeans_3d(
                              description="OptimizedKMeans/OptimizedMiniBatchKMeans"),
     k_clusters: int = Query(2, description="Number of clusters"),
     user_id: int = Query(0, description="User ID"),
-    request_id: int = Query(0, description="Request ID")
+    request_id: int = Query(0, description="Request ID"),
+    normalize: bool = Query(True, description="Normalize the data before clustering")
 ):
     """
     Endpoint for 3D KMeans clustering.
@@ -54,7 +55,8 @@ async def kmeans_3d(
             kmeans_type=kmeans_type,
             user_id=user_id,
             request_id=request_id,
-            selected_columns=[column1, column2, column3]
+            selected_columns=[column1, column2, column3],
+            normalize=normalize
         )
         # Return the KMeansResult3D object.
         return kmeans_result_3d
diff --git a/app/routers/job_router.py b/app/routers/job_router.py
index a86933e..058949c 100644
--- a/app/routers/job_router.py
+++ b/app/routers/job_router.py
@@ -3,6 +3,7 @@
 """
 import asyncio
 import logging
+import uuid
 from typing import Optional, Union, List
 import json
 
@@ -15,9 +16,12 @@
 from app.services.clustering_algorithms import CustomKMeans
 from app.services.clustering_service import process_and_cluster
 from app.database.connection import get_db
-from app.services.job_service import RunJob, list_jobs, create_job, get_job_by_id, get_job_by_name
+from app.services.job_service import (RunJob, list_jobs, create_job, get_job_by_id,
+                                      get_job_by_name, list_jobs_name)
 from app.models.job_model import UserJob, JobStatus, JobResponse, JobResponseFull
 from app.services.utils import save_temp_file, delete_file, load_dataframe
+from app.database.user_db import User
+from app.entitys.user import active_user, auth_backend, UserManager, get_user_manager
 
 router = APIRouter()
 
@@ -42,7 +46,8 @@ async def is_disconnected(socket: WebSocket):
 
 
 # pylint: disable=too-many-arguments
-def kmeans_job(data_frame, name, columns, k_cluster, distance_metric, cluster_count_determination):
+def kmeans_job(job_id, data_frame, name, columns, k_cluster, distance_metric,
+               cluster_count_determination):
     """
     Führt KMeans durch
     :param data_frame:
@@ -54,8 +59,13 @@ def kmeans_job(data_frame, name, columns, k_cluster, distance_metric, cluster_co
     :return: Ergebnis
     """
 
+    # Create file for method
+    with open(f"{TEMP_FILES_DIR}{uuid.uuid1()}.json", "w", encoding="utf-8") as outfile:
+        outfile.write(data_frame.to_json())
+        filename = outfile.name
+
     results = process_and_cluster(data_frame, cluster_count_determination, distance_metric,
-                                  columns, k_cluster)
+                                  columns, k_cluster, filename)
 
     # Determine method used
     if k_cluster:
@@ -65,8 +75,8 @@ def kmeans_job(data_frame, name, columns, k_cluster, distance_metric, cluster_co
 
     # Return clustering result model
     return ClusterResult(
-        user_id=0,
-        request_id=0,
+        user_id=-1,
+        request_id=job_id,
         name=name or "KMeans",
         # pylint: disable=duplicate-code
         cluster=results["cluster"],
@@ -81,19 +91,25 @@ def kmeans_job(data_frame, name, columns, k_cluster, distance_metric, cluster_co
 
 
 @router.websocket("/{job_id}/")
-async def job_websocket(web_socket: WebSocket, job_id: int, database: Session = Depends(get_db)):
+async def job_websocket(web_socket: WebSocket, job_id: int, database: Session = Depends(get_db),
+                        user_manager: UserManager = Depends(get_user_manager)):
     """
     Web-Socket für Jobs
-    :param web_socket:
-    :param job_id:
-    :param db:
-    :return:
     """
+
     await web_socket.accept()
 
+    # Auth
+    cookie = web_socket.cookies.get("fastapiusersauth")
+    user = await auth_backend.get_strategy().read_token(cookie, user_manager)
+    if not user or not user.is_active:
+        await web_socket.send_text("Unauthorized")
+        await web_socket.close()
+        return
+
     # Find job in database
     current_job = get_job_by_id(database, job_id)
-    if current_job is None:
+    if current_job is None or current_job.user_id != str(user.id):
         await web_socket.send_json({})
         await web_socket.close()
         return
@@ -123,7 +139,8 @@ async def job_websocket(web_socket: WebSocket, job_id: int, database: Session =
 
     # Run Job
     j = RunJob(func=kmeans_job,
-               args=[read_json(current_job.json_values), current_job.job_name]
+               args=[current_job.id,
+                     read_json(current_job.json_values), current_job.job_name]
                     + list(job_type.parameters.values()))
     run_job = asyncio.ensure_future(j.run_async())
 
@@ -135,7 +152,6 @@ async def job_websocket(web_socket: WebSocket, job_id: int, database: Session =
 
     # If disconnect -> cancel job
     if run_check.done() and run_check:
-        print("Client disconnected!")
         j.cancel()
         database.commit()
         return
@@ -153,10 +169,11 @@ async def job_websocket(web_socket: WebSocket, job_id: int, database: Session =
     await web_socket.close()
 
 
-# pylint: disable=too-many-arguments
+# pylint: disable=too-many-arguments too-many-locals
 @router.post("/create/kmeans", response_model=JobResponse)
 async def create_kmeans_job(
         database: Session = Depends(get_db),
+        user: User = Depends(active_user),
         file: UploadFile = File(...),
         column1
         : Optional[Union[str, int]] = None,
@@ -171,18 +188,11 @@ async def create_kmeans_job(
         cluster_count_determination: Optional[str] = Query(
             "ELBOW", alias="clusterDetermination",
             description="ELBOW, SILHOUETTE"
-        )
+        ),
+        name: str = "KMeans"
 ):
     """
     Erstellt einen Job für KMeans
-    :param database:
-    :param file:
-    :param column1:
-    :param column2:
-    :param k_cluster:
-    :param distance_metric:
-    :param cluster_count_determination:
-    :return:
     """
     # pylint: disable=duplicate-code
     # Validate distance metric
@@ -224,8 +234,8 @@ async def create_kmeans_job(
     finally:
         delete_file(file_path)
 
-    db_job = create_job(database, user_id=0, job_parameters=job_type.to_json(),
-                        json_input=data_frame.to_json())
+    db_job = create_job(database, user_id=user.id, job_parameters=job_type.to_json(),
+                        json_input=data_frame.to_json(), name=name)
 
     return JobResponse(job_id=db_job.id,
                        user_id=db_job.user_id,
@@ -238,7 +248,8 @@ async def create_kmeans_job(
 
 @router.get("/list/", response_model=Union[List[JobResponse], List[JobResponseFull]])
 async def job_list(job_id: Optional[int] = None, job_name: Optional[str] = None,
-                   with_values: Optional[bool] = False, database: Session = Depends(get_db)):
+                   with_values: Optional[bool] = False, database: Session = Depends(get_db),
+                   user: User = Depends(active_user)):
     """
     Gibt alle Jobs in der Datenbank als Json zurück. Optional
     können einzelne Jobs mit bestimmter ID
@@ -248,17 +259,17 @@ async def job_list(job_id: Optional[int] = None, job_name: Optional[str] = None,
     """
 
     if job_id is not None:
-        db_job = get_job_by_id(database, job_id)
-        if db_job is None:
+        db_job = get_job_by_id(database=database, job_id=job_id)
+        if db_job is None or db_job.user_id != str(user.id):
             raise HTTPException(status_code=404, detail="Job nicht gefunden!")
         list_content = [db_job, ]
     elif job_name is not None:
-        db_job = get_job_by_name(database, job_name)
+        db_job = get_job_by_name(database, job_name, str(user.id))
         if db_job is None:
             raise HTTPException(status_code=404, detail="Job nicht gefunden!")
-        list_content = [db_job, ]
+        list_content = list_jobs_name(database, str(user.id), job_name)
     else:
-        list_content = list_jobs(database)
+        list_content = list_jobs(database, str(user.id))
 
     if with_values:
         result = [JobResponseFull(job_id=db_job.id,
@@ -268,7 +279,7 @@ async def job_list(job_id: Optional[int] = None, job_name: Optional[str] = None,
                                   job_parameters=db_job.job_parameters,
                                   status=db_job.status,
                                   json_values=db_job.json_values
-                                  ) for db_job in list_jobs(database)]
+                                  ) for db_job in list_content]
     else:
         result = [JobResponse(job_id=db_job.id,
                               user_id=db_job.user_id,
diff --git a/app/services/advanced_kmeans_service.py b/app/services/advanced_kmeans_service.py
index dccd8d5..aa520f5 100644
--- a/app/services/advanced_kmeans_service.py
+++ b/app/services/advanced_kmeans_service.py
@@ -38,7 +38,8 @@ def perform_advanced_kmeans(
     kmeans_type: str,
     user_id: int,
     request_id: int,
-    selected_columns: Union[None, list[int]] = None
+    selected_columns: Union[None, list[int]] = None,
+    normalize: bool = True
 ) -> BasicKMeansResult:
     """
     Perform KMeans clustering on an uploaded file with automatic k determination.
@@ -48,11 +49,14 @@ def perform_advanced_kmeans(
     
     data_frame_cat = handle_categorical_data(data_frame)
     
-    data_frame_norm = normalize_dataframe(data_frame_cat)
+    if normalize:
+        data_frame_new = normalize_dataframe(data_frame_cat)
+    else:
+        data_frame_new = data_frame_cat
 
     # Determine the optimal k
     max_clusters = min(int(0.25 * data_frame.shape[0]), 20)
-    optimal_k = determine_optimal_k(data_frame_norm, max_clusters)
+    optimal_k = determine_optimal_k(data_frame_new, max_clusters)
     
     #print dataframe shape
     print(data_frame.shape)
@@ -65,6 +69,7 @@ def perform_advanced_kmeans(
         user_id=user_id,
         request_id=request_id,
         advanced_k=optimal_k,
-        filename=filename
+        filename=filename,
+        normalize=normalize
     )
     return result
diff --git a/app/services/basic_kmeans_service.py b/app/services/basic_kmeans_service.py
index f663b70..06e8040 100644
--- a/app/services/basic_kmeans_service.py
+++ b/app/services/basic_kmeans_service.py
@@ -10,40 +10,16 @@
 import logging
 from typing import Optional, Union
 import pandas as pd
-import numpy as np
 from fastapi import UploadFile
 
 from app.services.custom_kmeans import OptimizedKMeans, OptimizedMiniBatchKMeans
-from app.models.basic_kmeans_model import BasicKMeansResult, Cluster, Centroid
-from app.services.utils import process_uploaded_file,normalize_dataframe, handle_categorical_data
+from app.models.basic_kmeans_model import BasicKMeansResult
+from app.services.utils import (process_uploaded_file,normalize_dataframe, 
+                                handle_categorical_data, transform_to_2d_cluster_model)
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
-def transform_to_cluster_model(data_frame: pd.DataFrame, cluster_centers: np.ndarray) -> list:
-    """
-    Transform the data into the Cluster model structure.
-    """
-    clusters_list = []
-
-    for cluster_id in range(cluster_centers.shape[0]):
-        cluster_data = data_frame[data_frame["cluster"] == cluster_id].drop(columns=["cluster"])
-        
-        # Transform points to always have "x" and "y" as keys
-        cluster_points = [{"x": row[0], "y": row[1]} for _, row in cluster_data.iterrows()]
-        
-        clusters_list.append(
-            Cluster(
-                clusterNr=cluster_id,
-                centroid=Centroid(
-                    x=cluster_centers[cluster_id][0], y=cluster_centers[cluster_id][1]),
-                points=cluster_points
-            )
-        )
-
-    return clusters_list
-
-
 def perform_kmeans_from_file(
     file: UploadFile,
     distance_metric: str,
@@ -51,7 +27,8 @@ def perform_kmeans_from_file(
     user_id: int,
     request_id: int,
     selected_columns: Union[None, list[int]] = None,
-    user_k: Optional[int] = None
+    user_k: Optional[int] = None,
+    normalize: bool = True
 ) -> BasicKMeansResult:
     """
     Perform KMeans clustering on an uploaded file.
@@ -59,7 +36,7 @@ def perform_kmeans_from_file(
     data_frame, filename = process_uploaded_file(file, selected_columns)
     logger.info("Processed uploaded file. Shape: %s", data_frame.shape)
     return _perform_kmeans(data_frame, filename, distance_metric, 
-                           kmeans_type, user_id, request_id, user_k)
+                           kmeans_type, user_id, request_id, user_k, normalize)
 
 
 def perform_kmeans_from_dataframe(
@@ -69,13 +46,14 @@ def perform_kmeans_from_dataframe(
     kmeans_type: str,
     user_id: int,
     request_id: int,
-    advanced_k: Optional[int] = None
+    advanced_k: Optional[int] = None,
+    normalize: bool = True
 ) -> BasicKMeansResult:
     """
     Perform KMeans clustering on a DataFrame.
     """
     return _perform_kmeans(data_frame, filename, distance_metric, 
-                           kmeans_type, user_id, request_id, advanced_k)
+                           kmeans_type, user_id, request_id, advanced_k, normalize)
 
 # pylint: disable=R0801
 def _perform_kmeans(
@@ -85,11 +63,15 @@ def _perform_kmeans(
     kmeans_type: str,
     user_id: int,
     request_id: int,
-    k: int
+    k: int,
+    normalize: bool = True
 ) -> BasicKMeansResult:
-    data_frame=handle_categorical_data(data_frame)
+    data_frame_cat=handle_categorical_data(data_frame)
     
-    data_frame = normalize_dataframe(data_frame)
+    if normalize:
+        data_frame = normalize_dataframe(data_frame_cat)
+    else: 
+        data_frame = data_frame_cat
     
     data_np = data_frame.values
     
@@ -116,7 +98,7 @@ def _perform_kmeans(
     logger.info("Assigned labels to data.")
 
     # Transform the results to the Cluster model structure
-    clusters = transform_to_cluster_model(data_frame, model.cluster_centers_)
+    clusters = transform_to_2d_cluster_model(data_frame, model.cluster_centers_)
     logger.info("Transformed data to Cluster models.")
 
     x_label = data_frame.columns[0]
diff --git a/app/services/custom_kmeans.py b/app/services/custom_kmeans.py
index 749095a..9464b1d 100644
--- a/app/services/custom_kmeans.py
+++ b/app/services/custom_kmeans.py
@@ -69,7 +69,7 @@ class BaseOptimizedKMeans:
     supported_distance_metrics = {
         "EUCLIDEAN": euclidean_distance_matrix,
         "MANHATTAN": manhattan_distance_matrix,
-         "JACCARDS": jaccard_distance_matrix
+        "JACCARDS": jaccard_distance_matrix
     }
 
     def __init__(self, number_clusters, distance_metric="EUCLIDEAN",
@@ -181,13 +181,13 @@ class OptimizedMiniBatchKMeans(BaseOptimizedKMeans):
     Optimized Mini-Batch K-Means clustering with specified distance metric.
     """
     # pylint: disable=too-many-arguments
+
     def __init__(self, number_clusters, distance_metric="EUCLIDEAN",
                  batch_size=100, max_iterations=300, tolerance=1e-4):
         super().__init__(number_clusters, distance_metric, max_iterations, tolerance)
         self.batch_size = batch_size
 
     def fit(self, data_points):
-
         kmeans = KMeans(n_clusters=self.number_clusters,
                         init='k-means++', max_iter=1, n_init=1)
         kmeans.fit(data_points)
@@ -198,14 +198,21 @@ def fit(self, data_points):
                 data_points.shape[0], self.batch_size, replace=False)
             mini_batch = data_points[indices]
 
-            distances = np.array([[self.distance(point, center)
-                                   for center in self.cluster_centers_] for point in mini_batch])
+            distances = self.distance(mini_batch, self.cluster_centers_)
             labels = np.argmin(distances, axis=1)
 
-            new_centers = np.array([mini_batch[labels == i].mean(axis=0)
-                                    for i in range(self.number_clusters)])
-            if np.all(np.abs(new_centers - self.cluster_centers_) < self.tolerance):
+            for i in range(self.number_clusters):
+                points_in_cluster = mini_batch[labels == i]
+                if len(points_in_cluster) > 0:
+                    # Use a simple moving average for updating
+                    self.cluster_centers_[i] = (0.9 * self.cluster_centers_[i]
+                                                + 0.1 * points_in_cluster.mean(axis=0))
+
+            # Check for convergence
+            if np.all(np.abs(self.distance(self.cluster_centers_, 
+                                           self.cluster_centers_.copy()) < self.tolerance)):
+                logger.info(
+                    "Convergence reached after %d iterations.", _)
                 break
 
-            self.cluster_centers_ = new_centers
             self.iterations_ += 1
diff --git a/app/services/job_service.py b/app/services/job_service.py
index 005f2cf..afef487 100644
--- a/app/services/job_service.py
+++ b/app/services/job_service.py
@@ -16,14 +16,14 @@
 logger = logging.getLogger(__name__)
 
 
-def create_job(database: Session, user_id: int, job_parameters: str, json_input: str):
+def create_job(database: Session, user_id: int, job_parameters: str, json_input: str, name: str):
     """
     Erstellt einen Job in der Datenbank
     """
 
     db_job = DBJob(user_id=user_id, created_at=datetime.now(),
                    status=JobStatus.WAITING, json_values=json_input,
-                   job_parameters=job_parameters)
+                   job_parameters=job_parameters, job_name=name)
     database.add(db_job)
     database.commit()
     database.refresh(db_job)
@@ -38,11 +38,18 @@ def set_job_result(database: Session, job_id: int, result: str):
     database.commit()
 
 
-def list_jobs(database: Session, skip: int = 0, limit: int = 100):
+def list_jobs(database: Session, filter_user_id: str):
     """
     Gibt eine Liste von allen Jobs der Datenbank zurück
     """
-    return database.query(DBJob).offset(skip).limit(limit).all()
+    return database.query(DBJob).filter(DBJob.user_id == filter_user_id).all()
+
+def list_jobs_name(database: Session, filter_user_id: str, name: str):
+    """
+    Gibt eine Liste von allen Jobs mit dem Namen der Datenbank zurück
+    """
+    return database.query(DBJob).filter(DBJob.user_id == filter_user_id
+                                        and DBJob.job_name == name).all()
 
 
 def get_job_by_id(database: Session, job_id: int):
@@ -52,11 +59,12 @@ def get_job_by_id(database: Session, job_id: int):
     return database.query(DBJob).get(job_id)
 
 
-def get_job_by_name(database: Session, job_name: str):
+def get_job_by_name(database: Session, job_name: str, filter_user_id: str):
     """
     Gibt einen Job mit einer bestimmten ID zurück
     """
-    return database.query(DBJob).where(DBJob.job_name.is_(job_name)).first()
+    return (database.query(DBJob).filter(DBJob.user_id == filter_user_id)
+            .where(DBJob.job_name == job_name).all())
 
 
 class RunJob:
@@ -83,8 +91,6 @@ async def run_async(self):
         loop = asyncio.get_event_loop()
         fut = loop.create_future()
 
-        print(self._func.__name__ + f"({self._args})")
-
         def _on_done(obj):
             """
             Callback, nachdem der Job abgeschlossen wurde
diff --git a/app/services/n_d_advanced_kmeans_service.py b/app/services/n_d_advanced_kmeans_service.py
index 735a611..ec4d94c 100644
--- a/app/services/n_d_advanced_kmeans_service.py
+++ b/app/services/n_d_advanced_kmeans_service.py
@@ -7,25 +7,25 @@
 
 from typing import Union
 
-from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
 import pandas as pd
 from fastapi import UploadFile
 
-from app.models.basic_kmeans_model import KMeansResultND
+from app.models.basic_kmeans_model import BasicKMeansResult, KMeansResult3D
 from app.services.n_d_basic_kmeans_service import perform_nd_kmeans_from_dataframe
 from app.services.utils import process_uploaded_file, normalize_dataframe, handle_categorical_data
 from app.services.advanced_kmeans_service import determine_optimal_k
 
-# pylint: disable=too-many-arguments
-# pylint: disable=R0801
+# pylint: disable=too-many-arguments,too-many-locals
 def perform_advanced_nd_kmeans(
     file: UploadFile,
     distance_metric: str,
     kmeans_type: str,
     user_id: int,
     request_id: int,
-    selected_columns: Union[None, list[int]] = None
-) -> KMeansResultND:
+    selected_columns: Union[None, list[int]] = None,
+    use_3d_model: bool = False
+) -> Union[BasicKMeansResult, KMeansResult3D]:
     """
     Perform N-Dimensional KMeans clustering on an uploaded file with automatic k determination.
     """
@@ -38,22 +38,24 @@ def perform_advanced_nd_kmeans(
     # Normalize the dataframe
     data_frame_norm = normalize_dataframe(data_frame_converted)
 
-    # Reduce to 2D using PCA
-    pca = PCA(n_components=2)
-    data_2d = pca.fit_transform(data_frame_norm)
+    # Reduce to 2D or 3D using t-SNE based on the use_3d_model flag
+    n_components = 3 if use_3d_model else 2
+    tsne = TSNE(n_components=n_components, random_state=42)
+    data_reduced = tsne.fit_transform(data_frame_norm)
 
-    # Determine the optimal k in 2D space
-    max_clusters = min(int(0.25 * data_2d.shape[0]), 20)
-    optimal_k = determine_optimal_k(pd.DataFrame(data_2d), max_clusters)
+    # Determine the optimal k in reduced space (2D or 3D)
+    max_clusters = min(int(0.25 * data_reduced.shape[0]), 20)
+    optimal_k = determine_optimal_k(pd.DataFrame(data_reduced), max_clusters)
 
     # Use the n_d_basic_kmeans_service with the determined optimal k
     result = perform_nd_kmeans_from_dataframe(
-        data_frame=data_frame,
+        data_frame=data_frame_converted,
         distance_metric=distance_metric,
         kmeans_type=kmeans_type,
         user_id=user_id,
         request_id=request_id,
         user_k=optimal_k,
-        filename=filename
+        filename=filename,
+        use_3d_model=use_3d_model
     )
     return result
diff --git a/app/services/n_d_basic_kmeans_service.py b/app/services/n_d_basic_kmeans_service.py
index 07f1892..3782d45 100644
--- a/app/services/n_d_basic_kmeans_service.py
+++ b/app/services/n_d_basic_kmeans_service.py
@@ -1,116 +1,92 @@
 """
 n_d_basic_kmeans_service.py
 -----------------------
-Service for performing N-Dimensional KMeans clustering using optimized KMeans and MiniBatch KMeans, 
-reducing dimensionality to 2D using PCA.
+Service for performing N-Dimensional KMeans clustering using optimized KMeans and MiniBatch KMeans,
+reducing dimensionality using t-SNE.
 """
 
 import logging
-from typing import Optional, Union, Dict
+from typing import Optional, Union
 import pandas as pd
-import numpy as np
 from fastapi import UploadFile
-from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
 from app.services.custom_kmeans import OptimizedKMeans, OptimizedMiniBatchKMeans
-from app.models.basic_kmeans_model import KMeansResultND, Cluster, Centroid
-from app.services.utils import process_uploaded_file, normalize_dataframe, handle_categorical_data
+from app.models.basic_kmeans_model import BasicKMeansResult, KMeansResult3D
+from app.services.utils import (process_uploaded_file, 
+                                normalize_dataframe,
+                                handle_categorical_data, 
+                                transform_to_2d_cluster_model, 
+                                transform_to_3d_cluster_model)
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
-# pylint: disable=R0801
-def transform_to_2d_cluster_model(data_frame: pd.DataFrame, cluster_centers: np.ndarray) -> list:
-    """
-    Transform the data into the 2D Cluster model structure.
-    """
-    clusters_list = []
-    for cluster_id in range(cluster_centers.shape[0]):
-        cluster_data = data_frame[data_frame["cluster"] == cluster_id].drop(columns=[
-                                                                            "cluster"])
-        cluster_points = [{"x": row.iloc[0], "y": row.iloc[1]}
-                          for _, row in cluster_data.iterrows()]
-
-        clusters_list.append(
-            Cluster(
-                clusterNr=cluster_id,
-                centroid=Centroid(
-                    x=cluster_centers[cluster_id][0],
-                    y=cluster_centers[cluster_id][1]),
-                points=cluster_points
-            )
-        )
-    return clusters_list
-
-
-def extract_important_features(pca: PCA, n_features: int = 5) -> Dict[str, float]:
-    """
-    Extract the top n_features contributing to the PCA.
-    """
-    # Find the index of the important features based on explained_variance_ratio_
-    important_indices = np.argsort(pca.explained_variance_ratio_)[
-        ::-1][:n_features]
-    return {f"feature_{index}": importance for index,
-            importance in zip(important_indices, pca.explained_variance_ratio_[important_indices])}
-
+# pylint: disable=too-many-arguments
 
 
-# pylint: disable=too-many-arguments
 def perform_nd_kmeans_from_file(
-    file: UploadFile,
-    distance_metric: str,
-    kmeans_type: str,
-    user_id: int,
-    request_id: int,
-    selected_columns: Union[None, list[int]] = None,
-    user_k: Optional[int] = None
-) -> KMeansResultND:
+        file: UploadFile,
+        distance_metric: str,
+        kmeans_type: str,
+        user_id: int,
+        request_id: int,
+        selected_columns: Union[None, list[int]] = None,
+        user_k: Optional[int] = None,
+        use_3d_model: bool = False
+) -> Union[BasicKMeansResult, KMeansResult3D]:
     """
-    Perform N-Dimensional KMeans clustering on an uploaded file and reduce to 2D using PCA.
+    Perform N-Dimensional KMeans clustering on an uploaded 
+    file and reduce dimensionality using t-SNE.
     """
     data_frame, filename = process_uploaded_file(file, selected_columns)
     data_frame = handle_categorical_data(data_frame)
     return _perform_nd_kmeans(data_frame, filename, distance_metric,
-                              kmeans_type, user_id, request_id, user_k)
+                              kmeans_type, user_id, request_id, user_k, use_3d_model)
 
 
 # pylint: disable=too-many-arguments
 def perform_nd_kmeans_from_dataframe(
-    data_frame: pd.DataFrame,
-    filename: str,
-    distance_metric: str,
-    kmeans_type: str,
-    user_id: int,
-    request_id: int,
-    user_k: Optional[int] = None
-) -> KMeansResultND:
+        data_frame: pd.DataFrame,
+        filename: str,
+        distance_metric: str,
+        kmeans_type: str,
+        user_id: int,
+        request_id: int,
+        user_k: Optional[int] = None,
+        use_3d_model: bool = False
+) -> Union[BasicKMeansResult, KMeansResult3D]:
     """
-    Perform N-Dimensional KMeans clustering on a DataFrame and reduce to 2D using PCA.
+    Perform N-Dimensional KMeans clustering on a DataFrame and reduce dimensionality using t-SNE.
     """
     data_frame = handle_categorical_data(data_frame)
     return _perform_nd_kmeans(data_frame, filename, distance_metric,
-                              kmeans_type, user_id, request_id, user_k)
+                              kmeans_type, user_id, request_id, user_k, use_3d_model)
 
 
 def _perform_nd_kmeans(
-    data_frame: pd.DataFrame,
-    filename: str,
-    distance_metric: str,
-    kmeans_type: str,
-    user_id: int,
-    request_id: int,
-    k: int
-) -> KMeansResultND:
-
-    original_columns = list(data_frame.columns)
+        data_frame: pd.DataFrame,
+        filename: str,
+        distance_metric: str,
+        kmeans_type: str,
+        user_id: int,
+        request_id: int,
+        k: int,
+        use_3d_model: bool = False
+) -> Union[BasicKMeansResult, KMeansResult3D]:
+
     data_frame = normalize_dataframe(data_frame)
     data_np = data_frame.values
 
-    pca = PCA(n_components=2)
-    data_2d = pca.fit_transform(data_np)
+    components = 3 if use_3d_model else 2
+    tsne = TSNE(n_components=components)
+    data_transformed = tsne.fit_transform(data_np)
 
-    important_features = extract_important_features(pca)
-    important_features_mapped = {original_columns[int(
-        key.split("_")[1])]: value for key, value in important_features.items()}
+    if use_3d_model:
+        data_frame = pd.DataFrame(data_transformed, columns=[
+                                  't-SNE1', 't-SNE2', 't-SNE3'])
+    else:
+        data_frame = pd.DataFrame(
+            data_transformed, columns=['t-SNE1', 't-SNE2'])
 
     if kmeans_type == "OptimizedKMeans":
         model = OptimizedKMeans(k, distance_metric)
@@ -119,23 +95,37 @@ def _perform_nd_kmeans(
     else:
         raise ValueError(f"Invalid kmeans_type: {kmeans_type}")
 
-    logger.info(data_2d[:10])
-
-    model.fit(data_2d)
-    data_frame['cluster'] = model.assign_labels(data_2d)
+    logger.info(data_transformed[:10])
+
+    model.fit(data_transformed)
+    data_frame['cluster'] = model.assign_labels(data_transformed)
+
+    if use_3d_model:
+        clusters = transform_to_3d_cluster_model(
+            data_frame, model.cluster_centers_)
+        return KMeansResult3D(
+            user_id=user_id,
+            request_id=request_id,
+            cluster=clusters,
+            x_label="t-SNE1",
+            y_label="t-SNE2",
+            z_label="t-SNE3",
+            iterations=model.iterations_,
+            used_distance_metric=distance_metric,
+            name=filename,
+            k_value=k
+        )
 
     clusters = transform_to_2d_cluster_model(
         data_frame, model.cluster_centers_)
-
-    return KMeansResultND(
+    return BasicKMeansResult(
         user_id=user_id,
         request_id=request_id,
-        clusters=clusters,
-        x_label="PCA1",
-        y_label="PCA2",
+        cluster=clusters,
+        x_label="t-SNE1",
+        y_label="t-SNE2",
         iterations=model.iterations_,
         used_distance_metric=distance_metric,
         name=filename,
-        k_value=k,
-        important_features=important_features_mapped
+        k_value=k
     )
diff --git a/app/services/three_d_advanced_kmeans_service.py b/app/services/three_d_advanced_kmeans_service.py
index 1bc3af8..d31aa65 100644
--- a/app/services/three_d_advanced_kmeans_service.py
+++ b/app/services/three_d_advanced_kmeans_service.py
@@ -19,7 +19,8 @@ def perform_advanced_3d_kmeans(
     kmeans_type: str,
     user_id: int,
     request_id: int,
-    selected_columns: Union[None, list[int]] = None
+    selected_columns: Union[None, list[int]] = None,
+    normalize: bool = True
 ) -> KMeansResult3D:
     """
     Perform 3D KMeans clustering on an uploaded file with automatic k determination.
@@ -28,8 +29,12 @@ def perform_advanced_3d_kmeans(
     data_frame, filename = process_uploaded_file(file, selected_columns)
 
     data_frame_cat = handle_categorical_data(data_frame)
-    
-    data_frame_norm = normalize_dataframe(data_frame_cat)
+
+    if normalize:
+        data_frame_norm = normalize_dataframe(data_frame_cat)
+    else:
+        data_frame_norm = data_frame_cat
+
     # Determine the optimal k
     max_clusters = min(int(0.25 * data_frame.shape[0]), 20)
     optimal_k = determine_optimal_k(data_frame_norm, max_clusters)
@@ -42,6 +47,7 @@ def perform_advanced_3d_kmeans(
         user_id=user_id,
         request_id=request_id,
         advanced_k=optimal_k,
-        filename=filename
+        filename=filename,
+        normalize=normalize
     )
     return result
diff --git a/app/services/three_d_basic_kmeans_service.py b/app/services/three_d_basic_kmeans_service.py
index a73ad45..e19659b 100644
--- a/app/services/three_d_basic_kmeans_service.py
+++ b/app/services/three_d_basic_kmeans_service.py
@@ -7,44 +7,16 @@
 import logging
 from typing import Optional, Union
 import pandas as pd
-import numpy as np
 from fastapi import UploadFile
 from app.services.custom_kmeans import OptimizedKMeans, OptimizedMiniBatchKMeans
-from app.models.basic_kmeans_model import KMeansResult3D, Cluster3D, Centroid3D
-from app.services.utils import process_uploaded_file, normalize_dataframe, handle_categorical_data
+from app.models.basic_kmeans_model import KMeansResult3D
+from app.services.utils import (process_uploaded_file, normalize_dataframe, 
+                                handle_categorical_data, transform_to_3d_cluster_model)
 
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
 
-def transform_to_3d_cluster_model(data_frame: pd.DataFrame, cluster_centers: np.ndarray) -> list:
-    """
-    Transform the data into the 3D Cluster model structure.
-    """
-    clusters_list = []
-
-    for cluster_id in range(cluster_centers.shape[0]):
-        cluster_data = data_frame[data_frame["cluster"] == cluster_id].drop(columns=[
-                                                                            "cluster"])
-
-        # Transform points to always have "x", "y", and "z" as keys
-        cluster_points = [{"x": row[0], "y": row[1], "z": row[2]}
-                          for _, row in cluster_data.iterrows()]
-
-        clusters_list.append(
-            Cluster3D(
-                clusterNr=cluster_id,
-                centroid=Centroid3D(
-                    x=cluster_centers[cluster_id][0],
-                    y=cluster_centers[cluster_id][1],
-                    z=cluster_centers[cluster_id][2]),
-                points=cluster_points
-            )
-        )
-
-    return clusters_list
-
-
 # pylint: disable=too-many-arguments
 def perform_3d_kmeans_from_file(
     file: UploadFile,
@@ -53,7 +25,8 @@ def perform_3d_kmeans_from_file(
     user_id: int,
     request_id: int,
     selected_columns: Union[None, list[int]] = None,
-    user_k: Optional[int] = None
+    user_k: Optional[int] = None,
+    normalize: bool = True
 ) -> KMeansResult3D:
     """
     Perform 3D KMeans clustering on an uploaded file.
@@ -65,7 +38,7 @@ def perform_3d_kmeans_from_file(
     
     logger.info("Processed uploaded file. Shape: %s", data_frame.shape)
     return _perform_3d_kmeans(data_frame, filename, distance_metric,
-                              kmeans_type, user_id, request_id, user_k)
+                              kmeans_type, user_id, request_id, user_k, normalize)
 
 # pylint: disable=too-many-arguments
 
@@ -77,13 +50,16 @@ def perform_3d_kmeans_from_dataframe(
     kmeans_type: str,
     user_id: int,
     request_id: int,
-    advanced_k: Optional[int] = None
+    advanced_k: Optional[int] = None,
+    normalize: bool = True
 ) -> KMeansResult3D:
     """
     Perform 3D KMeans clustering on a DataFrame.
     """
+    data_frame = handle_categorical_data(data_frame)
+
     return _perform_3d_kmeans(data_frame, filename, distance_metric,
-                              kmeans_type, user_id, request_id, advanced_k)
+                              kmeans_type, user_id, request_id, advanced_k, normalize)
 
 # pylint: disable=R0801
 # pylint: disable=too-many-arguments
@@ -94,10 +70,14 @@ def _perform_3d_kmeans(
     kmeans_type: str,
     user_id: int,
     request_id: int,
-    k: int
+    k: int,
+    normalize: bool = True
 ) -> KMeansResult3D:
     # Convert DataFrame to numpy array for clustering
-    data_frame = normalize_dataframe(data_frame)
+    if normalize:
+        data_frame = normalize_dataframe(data_frame)
+    
+    data_np = data_frame.values
     data_np = data_frame.values
     logger.info("Converted data to numpy array. Shape: %s", data_np.shape)
 
diff --git a/app/services/utils.py b/app/services/utils.py
index b59934f..a2a2838 100644
--- a/app/services/utils.py
+++ b/app/services/utils.py
@@ -5,11 +5,14 @@
 import logging
 import os
 from typing import List, Union
+import numpy as np
 
 from fastapi import HTTPException, UploadFile
 import pandas as pd
 from sklearn.preprocessing import StandardScaler
 
+from app.models.basic_kmeans_model import Cluster, Centroid, Cluster3D, Centroid3D
+
 # Constants in uppercase
 CSV = '.csv'
 XLSX = '.xlsx'
@@ -141,6 +144,7 @@ def save_temp_file(file, directory):
 
     return file_path
 
+
 def handle_errors(error):
     """
     Error handling function.
@@ -151,7 +155,8 @@ def handle_errors(error):
     logging.error("Error processing file: %s", error)
     raise HTTPException(500, "Error processing file") from error
 
-def process_uploaded_file(file: UploadFile, 
+
+def process_uploaded_file(file: UploadFile,
                           selected_columns: Union[None, list[int]] = None) -> (pd.DataFrame, str):
     """
     Load, save, clean, and optionally select specific columns from the uploaded file. 
@@ -167,7 +172,7 @@ def process_uploaded_file(file: UploadFile,
     temp_file_path = save_temp_file(file, "temp/")
     data_frame = load_dataframe(temp_file_path)
     data_frame = clean_dataframe(data_frame)
-    
+
     # Select specific columns if provided
     if selected_columns:
         data_frame = extract_selected_columns(data_frame, selected_columns)
@@ -175,12 +180,14 @@ def process_uploaded_file(file: UploadFile,
     delete_file(temp_file_path)
     return data_frame, file.filename
 
+
 def handle_categorical_data(data_frame: pd.DataFrame) -> pd.DataFrame:
     """
     Convert categorical and boolean columns to numerical format using one-hot encoding.
     """
     return pd.get_dummies(data_frame, drop_first=True)
 
+
 def normalize_dataframe(dataframe: pd.DataFrame) -> pd.DataFrame:
     """
     Normalize the data using StandardScaler.
@@ -189,3 +196,57 @@ def normalize_dataframe(dataframe: pd.DataFrame) -> pd.DataFrame:
     normalized_data = scaler.fit_transform(dataframe)
     normalized_df = pd.DataFrame(normalized_data, columns=dataframe.columns)
     return normalized_df
+
+
+def transform_to_2d_cluster_model(data_frame: pd.DataFrame, cluster_centers: np.ndarray) -> list:
+    """
+    Transform the data into the Cluster model structure.
+    """
+    clusters_list = []
+
+    for cluster_id in range(cluster_centers.shape[0]):
+        cluster_data = data_frame[data_frame["cluster"] == cluster_id].drop(columns=[
+                                                                            "cluster"])
+
+        # Transform points to always have "x" and "y" as keys
+        cluster_points = [{"x": row.iloc[0], "y": row.iloc[1]}
+                          for _, row in cluster_data.iterrows()]
+
+        clusters_list.append(
+            Cluster(
+                clusterNr=cluster_id,
+                centroid=Centroid(
+                    x=cluster_centers[cluster_id][0], y=cluster_centers[cluster_id][1]),
+                points=cluster_points
+            )
+        )
+
+    return clusters_list
+
+
+def transform_to_3d_cluster_model(data_frame: pd.DataFrame, cluster_centers: np.ndarray) -> list:
+    """
+    Transform the data into the 3D Cluster model structure.
+    """
+    clusters_list = []
+
+    for cluster_id in range(cluster_centers.shape[0]):
+        cluster_data = data_frame[data_frame["cluster"] == cluster_id].drop(columns=[
+                                                                            "cluster"])
+
+        # Transform points to always have "x", "y", and "z" as keys
+        cluster_points = [{"x": row.iloc[0], "y": row.iloc[1], "z": row.iloc[2]}
+                          for _, row in cluster_data.iterrows()]
+
+        clusters_list.append(
+            Cluster3D(
+                clusterNr=cluster_id,
+                centroid=Centroid3D(
+                    x=cluster_centers[cluster_id][0],
+                    y=cluster_centers[cluster_id][1],
+                    z=cluster_centers[cluster_id][2]),
+                points=cluster_points
+            )
+        )
+
+    return clusters_list
diff --git a/notebooks/decision_tree b/notebooks/decision_tree
new file mode 100644
index 0000000..a3e8f4f
--- /dev/null
+++ b/notebooks/decision_tree
@@ -0,0 +1,6183 @@
+digraph {
+	140363292071296 [label="Is feature alcohol(f_id: 10) <= 10.8?
+alcohol
+Treshold: 10.8"]
+	140363292071872 [label="Is feature volatile acidity(f_id: 1) <= 0.265?
+volatile acidity
+Treshold: 0.265"]
+	140363292071296 -> 140363292071872
+	140363292075136 [label="Is feature volatile acidity(f_id: 1) <= 0.205?
+volatile acidity
+Treshold: 0.205"]
+	140363292071872 -> 140363292075136
+	140363292685824 [label="Is feature alcohol(f_id: 10) <= 8.9?
+alcohol
+Treshold: 8.9"]
+	140363292075136 -> 140363292685824
+	140363292080768 [label="Is feature free sulfur dioxide(f_id: 5) <= 33.0?
+free sulfur dioxide
+Treshold: 33.0"]
+	140363292685824 -> 140363292080768
+	140363292306560 [label="Is feature free sulfur dioxide(f_id: 5) <= 24.0?
+free sulfur dioxide
+Treshold: 24.0"]
+	140363292080768 -> 140363292306560
+	140364077938624 [label="Class: 6"]
+	140363292306560 -> 140364077938624
+	140363293445632 [label="Class: 7"]
+	140363292306560 -> 140363293445632
+	140363290495360 [label="Is feature residual sugar(f_id: 3) <= 13.8?
+residual sugar
+Treshold: 13.8"]
+	140363292080768 -> 140363290495360
+	140364077938688 [label="Is feature chlorides(f_id: 4) <= 0.042?
+chlorides
+Treshold: 0.042"]
+	140363290495360 -> 140364077938688
+	140363291549120 [label="Is feature sulphates(f_id: 9) <= 0.46?
+sulphates
+Treshold: 0.46"]
+	140364077938688 -> 140363291549120
+	140363292383680 [label="Class: 5"]
+	140363291549120 -> 140363292383680
+	140363290752064 [label="Class: 8"]
+	140363291549120 -> 140363290752064
+	140363292382912 [label="Is feature pH(f_id: 8) <= 3.34?
+pH
+Treshold: 3.34"]
+	140364077938688 -> 140363292382912
+	140363293229952 [label="Class: 6"]
+	140363292382912 -> 140363293229952
+	140363290551040 [label="Class: 5"]
+	140363292382912 -> 140363290551040
+	140363292384512 [label="Is feature residual sugar(f_id: 3) <= 14.2?
+residual sugar
+Treshold: 14.2"]
+	140363290495360 -> 140363292384512
+	140363290722752 [label="Class: 8"]
+	140363292384512 -> 140363290722752
+	140363290724544 [label="Class: 6"]
+	140363292384512 -> 140363290724544
+	140363290722432 [label="Is feature residual sugar(f_id: 3) <= 5.6?
+residual sugar
+Treshold: 5.6"]
+	140363292685824 -> 140363290722432
+	140363290732544 [label="Is feature free sulfur dioxide(f_id: 5) <= 10.0?
+free sulfur dioxide
+Treshold: 10.0"]
+	140363290722432 -> 140363290732544
+	140363290727296 [label="Is feature residual sugar(f_id: 3) <= 2.9?
+residual sugar
+Treshold: 2.9"]
+	140363290732544 -> 140363290727296
+	140363290738304 [label="Is feature chlorides(f_id: 4) <= 0.15?
+chlorides
+Treshold: 0.15"]
+	140363290727296 -> 140363290738304
+	140363290732672 [label="Is feature fixed acidity(f_id: 0) <= 8.9?
+fixed acidity
+Treshold: 8.9"]
+	140363290738304 -> 140363290732672
+	140363290725952 [label="Is feature density(f_id: 7) <= 0.99126?
+density
+Treshold: 0.99126"]
+	140363290732672 -> 140363290725952
+	140363290724160 [label="Is feature chlorides(f_id: 4) <= 0.027?
+chlorides
+Treshold: 0.027"]
+	140363290725952 -> 140363290724160
+	140363290884544 [label="Class: 5"]
+	140363290724160 -> 140363290884544
+	140364077408320 [label="Class: 4"]
+	140363290724160 -> 140364077408320
+	140363290734720 [label="Class: 5"]
+	140363290725952 -> 140363290734720
+	140363290493504 [label="Class: 3"]
+	140363290732672 -> 140363290493504
+	140363292711488 [label="Class: 6"]
+	140363290738304 -> 140363292711488
+	140363292834112 [label="Class: 6"]
+	140363290727296 -> 140363292834112
+	140363290746688 [label="Is feature sulphates(f_id: 9) <= 0.53?
+sulphates
+Treshold: 0.53"]
+	140363290732544 -> 140363290746688
+	140363290449088 [label="Is feature fixed acidity(f_id: 0) <= 7.8?
+fixed acidity
+Treshold: 7.8"]
+	140363290746688 -> 140363290449088
+	140363293123072 [label="Is feature pH(f_id: 8) <= 3.29?
+pH
+Treshold: 3.29"]
+	140363290449088 -> 140363293123072
+	140363293213568 [label="Is feature free sulfur dioxide(f_id: 5) <= 20.0?
+free sulfur dioxide
+Treshold: 20.0"]
+	140363293123072 -> 140363293213568
+	140363290612544 [label="Is feature chlorides(f_id: 4) <= 0.034?
+chlorides
+Treshold: 0.034"]
+	140363293213568 -> 140363290612544
+	140364076957632 [label="Is feature pH(f_id: 8) <= 3.19?
+pH
+Treshold: 3.19"]
+	140363290612544 -> 140364076957632
+	140364076952064 [label="Class: 7"]
+	140364076957632 -> 140364076952064
+	140364076949632 [label="Class: 8"]
+	140364076957632 -> 140364076949632
+	140364076953280 [label="Class: 6"]
+	140363290612544 -> 140364076953280
+	140364076951296 [label="Is feature residual sugar(f_id: 3) <= 2.0?
+residual sugar
+Treshold: 2.0"]
+	140363293213568 -> 140364076951296
+	140364076956800 [label="Is feature total sulfur dioxide(f_id: 6) <= 130.0?
+total sulfur dioxide
+Treshold: 130.0"]
+	140364076951296 -> 140364076956800
+	140364076955648 [label="Is feature citric acid(f_id: 2) <= 0.41?
+citric acid
+Treshold: 0.41"]
+	140364076956800 -> 140364076955648
+	140364076954880 [label="Is feature fixed acidity(f_id: 0) <= 7.0?
+fixed acidity
+Treshold: 7.0"]
+	140364076955648 -> 140364076954880
+	140364083987200 [label="Is feature citric acid(f_id: 2) <= 0.29?
+citric acid
+Treshold: 0.29"]
+	140364076954880 -> 140364083987200
+	140364076946560 [label="Class: 5"]
+	140364083987200 -> 140364076946560
+	140363290609920 [label="Is feature citric acid(f_id: 2) <= 0.4?
+citric acid
+Treshold: 0.4"]
+	140364083987200 -> 140363290609920
+	140363293349632 [label="Class: 6"]
+	140363290609920 -> 140363293349632
+	140363293227968 [label="Class: 5"]
+	140363290609920 -> 140363293227968
+	140363293223872 [label="Class: 5"]
+	140364076954880 -> 140363293223872
+	140363290619456 [label="Is feature pH(f_id: 8) <= 3.15?
+pH
+Treshold: 3.15"]
+	140364076955648 -> 140363290619456
+	140363292095296 [label="Class: 7"]
+	140363290619456 -> 140363292095296
+	140363290878016 [label="Is feature residual sugar(f_id: 3) <= 1.1?
+residual sugar
+Treshold: 1.1"]
+	140363290619456 -> 140363290878016
+	140363290883712 [label="Class: 5"]
+	140363290878016 -> 140363290883712
+	140363290873216 [label="Class: 6"]
+	140363290878016 -> 140363290873216
+	140363290872960 [label="Is feature total sulfur dioxide(f_id: 6) <= 147.0?
+total sulfur dioxide
+Treshold: 147.0"]
+	140364076956800 -> 140363290872960
+	140363290871936 [label="Is feature volatile acidity(f_id: 1) <= 0.16?
+volatile acidity
+Treshold: 0.16"]
+	140363290872960 -> 140363290871936
+	140363290874240 [label="Class: 7"]
+	140363290871936 -> 140363290874240
+	140363290883520 [label="Is feature density(f_id: 7) <= 0.993?
+density
+Treshold: 0.993"]
+	140363290871936 -> 140363290883520
+	140363290871104 [label="Class: 6"]
+	140363290883520 -> 140363290871104
+	140363290882624 [label="Is feature chlorides(f_id: 4) <= 0.091?
+chlorides
+Treshold: 0.091"]
+	140363290883520 -> 140363290882624
+	140364083892992 [label="Class: 7"]
+	140363290882624 -> 140364083892992
+	140364076960640 [label="Class: 6"]
+	140363290882624 -> 140364076960640
+	140363290915008 [label="Is feature pH(f_id: 8) <= 3.08?
+pH
+Treshold: 3.08"]
+	140363290872960 -> 140363290915008
+	140363290960768 [label="Class: 5"]
+	140363290915008 -> 140363290960768
+	140363290904640 [label="Is feature total sulfur dioxide(f_id: 6) <= 168.0?
+total sulfur dioxide
+Treshold: 168.0"]
+	140363290915008 -> 140363290904640
+	140363290907264 [label="Class: 6"]
+	140363290904640 -> 140363290907264
+	140363290910720 [label="Class: 5"]
+	140363290904640 -> 140363290910720
+	140363290904064 [label="Is feature density(f_id: 7) <= 0.99422?
+density
+Treshold: 0.99422"]
+	140364076951296 -> 140363290904064
+	140363290912448 [label="Class: 6"]
+	140363290904064 -> 140363290912448
+	140363290916800 [label="Is feature density(f_id: 7) <= 0.9949?
+density
+Treshold: 0.9949"]
+	140363290904064 -> 140363290916800
+	140363292432320 [label="Class: 5"]
+	140363290916800 -> 140363292432320
+	140363290609536 [label="Is feature residual sugar(f_id: 3) <= 4.6?
+residual sugar
+Treshold: 4.6"]
+	140363290916800 -> 140363290609536
+	140363290880896 [label="Class: 6"]
+	140363290609536 -> 140363290880896
+	140363290871232 [label="Class: 5"]
+	140363290609536 -> 140363290871232
+	140363290878528 [label="Is feature free sulfur dioxide(f_id: 5) <= 27.0?
+free sulfur dioxide
+Treshold: 27.0"]
+	140363293123072 -> 140363290878528
+	140363290917760 [label="Is feature alcohol(f_id: 10) <= 10.6?
+alcohol
+Treshold: 10.6"]
+	140363290878528 -> 140363290917760
+	140363290910976 [label="Is feature volatile acidity(f_id: 1) <= 0.135?
+volatile acidity
+Treshold: 0.135"]
+	140363290917760 -> 140363290910976
+	140363290905536 [label="Class: 6"]
+	140363290910976 -> 140363290905536
+	140363290902912 [label="Is feature pH(f_id: 8) <= 3.37?
+pH
+Treshold: 3.37"]
+	140363290910976 -> 140363290902912
+	140363290912000 [label="Class: 5"]
+	140363290902912 -> 140363290912000
+	140363292441344 [label="Is feature total sulfur dioxide(f_id: 6) <= 157.0?
+total sulfur dioxide
+Treshold: 157.0"]
+	140363290902912 -> 140363292441344
+	140363292439040 [label="Is feature sulphates(f_id: 9) <= 0.43?
+sulphates
+Treshold: 0.43"]
+	140363292441344 -> 140363292439040
+	140363292437824 [label="Is feature fixed acidity(f_id: 0) <= 7.2?
+fixed acidity
+Treshold: 7.2"]
+	140363292439040 -> 140363292437824
+	140363292434048 [label="Class: 5"]
+	140363292437824 -> 140363292434048
+	140363292428672 [label="Class: 6"]
+	140363292437824 -> 140363292428672
+	140364083888960 [label="Class: 6"]
+	140363292439040 -> 140364083888960
+	140364083889728 [label="Class: 5"]
+	140363292441344 -> 140364083889728
+	140363292083712 [label="Class: 7"]
+	140363290917760 -> 140363292083712
+	140363290958080 [label="Is feature density(f_id: 7) <= 0.99238?
+density
+Treshold: 0.99238"]
+	140363290878528 -> 140363290958080
+	140363290961984 [label="Class: 6"]
+	140363290958080 -> 140363290961984
+	140363290959424 [label="Is feature density(f_id: 7) <= 0.99254?
+density
+Treshold: 0.99254"]
+	140363290958080 -> 140363290959424
+	140363290963328 [label="Class: 5"]
+	140363290959424 -> 140363290963328
+	140363290959168 [label="Is feature citric acid(f_id: 2) <= 0.21?
+citric acid
+Treshold: 0.21"]
+	140363290959424 -> 140363290959168
+	140363290964544 [label="Class: 5"]
+	140363290959168 -> 140363290964544
+	140363290952064 [label="Is feature density(f_id: 7) <= 0.9939?
+density
+Treshold: 0.9939"]
+	140363290959168 -> 140363290952064
+	140363290956032 [label="Is feature sulphates(f_id: 9) <= 0.48?
+sulphates
+Treshold: 0.48"]
+	140363290952064 -> 140363290956032
+	140363290958528 [label="Is feature citric acid(f_id: 2) <= 0.28?
+citric acid
+Treshold: 0.28"]
+	140363290956032 -> 140363290958528
+	140363290954752 [label="Class: 7"]
+	140363290958528 -> 140363290954752
+	140363290960192 [label="Is feature residual sugar(f_id: 3) <= 1.2?
+residual sugar
+Treshold: 1.2"]
+	140363290958528 -> 140363290960192
+	140363290962816 [label="Class: 6"]
+	140363290960192 -> 140363290962816
+	140363290965632 [label="Is feature residual sugar(f_id: 3) <= 5.2?
+residual sugar
+Treshold: 5.2"]
+	140363290960192 -> 140363290965632
+	140363290956480 [label="Class: 7"]
+	140363290965632 -> 140363290956480
+	140363290962560 [label="Class: 6"]
+	140363290965632 -> 140363290962560
+	140363290959360 [label="Is feature free sulfur dioxide(f_id: 5) <= 30.0?
+free sulfur dioxide
+Treshold: 30.0"]
+	140363290956032 -> 140363290959360
+	140363290967424 [label="Class: 7"]
+	140363290959360 -> 140363290967424
+	140363290956800 [label="Class: 6"]
+	140363290959360 -> 140363290956800
+	140363290961600 [label="Is feature total sulfur dioxide(f_id: 6) <= 120.0?
+total sulfur dioxide
+Treshold: 120.0"]
+	140363290952064 -> 140363290961600
+	140363290964992 [label="Class: 7"]
+	140363290961600 -> 140363290964992
+	140363290967744 [label="Is feature volatile acidity(f_id: 1) <= 0.15?
+volatile acidity
+Treshold: 0.15"]
+	140363290961600 -> 140363290967744
+	140363290958336 [label="Is feature alcohol(f_id: 10) <= 9.6?
+alcohol
+Treshold: 9.6"]
+	140363290967744 -> 140363290958336
+	140363290955520 [label="Is feature chlorides(f_id: 4) <= 0.055?
+chlorides
+Treshold: 0.055"]
+	140363290958336 -> 140363290955520
+	140363290965440 [label="Class: 5"]
+	140363290955520 -> 140363290965440
+	140363290962624 [label="Is feature pH(f_id: 8) <= 3.37?
+pH
+Treshold: 3.37"]
+	140363290955520 -> 140363290962624
+	140363290960384 [label="Class: 6"]
+	140363290962624 -> 140363290960384
+	140363290967872 [label="Class: 7"]
+	140363290962624 -> 140363290967872
+	140363290963520 [label="Class: 6"]
+	140363290958336 -> 140363290963520
+	140363290965248 [label="Is feature density(f_id: 7) <= 0.9944?
+density
+Treshold: 0.9944"]
+	140363290967744 -> 140363290965248
+	140363290965120 [label="Class: 7"]
+	140363290965248 -> 140363290965120
+	140363290952832 [label="Class: 5"]
+	140363290965248 -> 140363290952832
+	140363290964736 [label="Is feature citric acid(f_id: 2) <= 0.31?
+citric acid
+Treshold: 0.31"]
+	140363290449088 -> 140363290964736
+	140363290965056 [label="Is feature free sulfur dioxide(f_id: 5) <= 26.0?
+free sulfur dioxide
+Treshold: 26.0"]
+	140363290964736 -> 140363290965056
+	140363290953984 [label="Class: 5"]
+	140363290965056 -> 140363290953984
+	140363290956992 [label="Is feature free sulfur dioxide(f_id: 5) <= 37.0?
+free sulfur dioxide
+Treshold: 37.0"]
+	140363290965056 -> 140363290956992
+	140363290954944 [label="Class: 4"]
+	140363290956992 -> 140363290954944
+	140363290963136 [label="Is feature pH(f_id: 8) <= 3.24?
+pH
+Treshold: 3.24"]
+	140363290956992 -> 140363290963136
+	140363290964416 [label="Class: 5"]
+	140363290963136 -> 140363290964416
+	140363290953088 [label="Class: 6"]
+	140363290963136 -> 140363290953088
+	140363290964352 [label="Is feature residual sugar(f_id: 3) <= 1.5?
+residual sugar
+Treshold: 1.5"]
+	140363290964736 -> 140363290964352
+	140363290957632 [label="Is feature volatile acidity(f_id: 1) <= 0.18?
+volatile acidity
+Treshold: 0.18"]
+	140363290964352 -> 140363290957632
+	140363290953600 [label="Is feature volatile acidity(f_id: 1) <= 0.16?
+volatile acidity
+Treshold: 0.16"]
+	140363290957632 -> 140363290953600
+	140363290952384 [label="Is feature fixed acidity(f_id: 0) <= 8.0?
+fixed acidity
+Treshold: 8.0"]
+	140363290953600 -> 140363290952384
+	140363290961792 [label="Class: 7"]
+	140363290952384 -> 140363290961792
+	140363290958720 [label="Class: 6"]
+	140363290952384 -> 140363290958720
+	140363290961664 [label="Class: 7"]
+	140363290953600 -> 140363290961664
+	140363290966912 [label="Is feature free sulfur dioxide(f_id: 5) <= 29.0?
+free sulfur dioxide
+Treshold: 29.0"]
+	140363290957632 -> 140363290966912
+	140363291002752 [label="Class: 6"]
+	140363290966912 -> 140363291002752
+	140363291009920 [label="Is feature fixed acidity(f_id: 0) <= 8.1?
+fixed acidity
+Treshold: 8.1"]
+	140363290966912 -> 140363291009920
+	140363291008576 [label="Class: 6"]
+	140363291009920 -> 140363291008576
+	140363291015680 [label="Class: 4"]
+	140363291009920 -> 140363291015680
+	140363291017152 [label="Is feature fixed acidity(f_id: 0) <= 8.6?
+fixed acidity
+Treshold: 8.6"]
+	140363290964352 -> 140363291017152
+	140363291008512 [label="Is feature free sulfur dioxide(f_id: 5) <= 24.0?
+free sulfur dioxide
+Treshold: 24.0"]
+	140363291017152 -> 140363291008512
+	140363291011584 [label="Is feature residual sugar(f_id: 3) <= 1.6?
+residual sugar
+Treshold: 1.6"]
+	140363291008512 -> 140363291011584
+	140363291006656 [label="Class: 5"]
+	140363291011584 -> 140363291006656
+	140363291014592 [label="Is feature chlorides(f_id: 4) <= 0.05?
+chlorides
+Treshold: 0.05"]
+	140363291011584 -> 140363291014592
+	140363291011968 [label="Class: 7"]
+	140363291014592 -> 140363291011968
+	140363291011648 [label="Class: 5"]
+	140363291014592 -> 140363291011648
+	140363291012352 [label="Is feature volatile acidity(f_id: 1) <= 0.19?
+volatile acidity
+Treshold: 0.19"]
+	140363291008512 -> 140363291012352
+	140363291015360 [label="Class: 6"]
+	140363291012352 -> 140363291015360
+	140363291014400 [label="Is feature density(f_id: 7) <= 0.9938?
+density
+Treshold: 0.9938"]
+	140363291012352 -> 140363291014400
+	140363291012672 [label="Class: 5"]
+	140363291014400 -> 140363291012672
+	140363291003712 [label="Is feature density(f_id: 7) <= 0.9939?
+density
+Treshold: 0.9939"]
+	140363291014400 -> 140363291003712
+	140363291003008 [label="Class: 6"]
+	140363291003712 -> 140363291003008
+	140363291005056 [label="Class: 7"]
+	140363291003712 -> 140363291005056
+	140363291013056 [label="Is feature density(f_id: 7) <= 0.9948?
+density
+Treshold: 0.9948"]
+	140363291017152 -> 140363291013056
+	140363291004032 [label="Class: 6"]
+	140363291013056 -> 140363291004032
+	140363291013760 [label="Class: 4"]
+	140363291013056 -> 140363291013760
+	140363291004416 [label="Is feature free sulfur dioxide(f_id: 5) <= 25.0?
+free sulfur dioxide
+Treshold: 25.0"]
+	140363290746688 -> 140363291004416
+	140363291009088 [label="Is feature fixed acidity(f_id: 0) <= 6.7?
+fixed acidity
+Treshold: 6.7"]
+	140363291004416 -> 140363291009088
+	140363291009216 [label="Is feature density(f_id: 7) <= 0.9927?
+density
+Treshold: 0.9927"]
+	140363291009088 -> 140363291009216
+	140363291014784 [label="Is feature residual sugar(f_id: 3) <= 1.3?
+residual sugar
+Treshold: 1.3"]
+	140363291009216 -> 140363291014784
+	140363291007168 [label="Class: 7"]
+	140363291014784 -> 140363291007168
+	140363291013952 [label="Class: 6"]
+	140363291014784 -> 140363291013952
+	140363291003136 [label="Class: 6"]
+	140363291009216 -> 140363291003136
+	140363291010048 [label="Is feature volatile acidity(f_id: 1) <= 0.15?
+volatile acidity
+Treshold: 0.15"]
+	140363291009088 -> 140363291010048
+	140363291007872 [label="Class: 5"]
+	140363291010048 -> 140363291007872
+	140363291006976 [label="Is feature residual sugar(f_id: 3) <= 1.6?
+residual sugar
+Treshold: 1.6"]
+	140363291010048 -> 140363291006976
+	140363291003264 [label="Is feature chlorides(f_id: 4) <= 0.049?
+chlorides
+Treshold: 0.049"]
+	140363291006976 -> 140363291003264
+	140363291010816 [label="Class: 6"]
+	140363291003264 -> 140363291010816
+	140363291010176 [label="Class: 5"]
+	140363291003264 -> 140363291010176
+	140363291012928 [label="Is feature total sulfur dioxide(f_id: 6) <= 136.0?
+total sulfur dioxide
+Treshold: 136.0"]
+	140363291006976 -> 140363291012928
+	140363291001536 [label="Class: 4"]
+	140363291012928 -> 140363291001536
+	140363291004096 [label="Class: 6"]
+	140363291012928 -> 140363291004096
+	140363291013696 [label="Is feature citric acid(f_id: 2) <= 0.24?
+citric acid
+Treshold: 0.24"]
+	140363291004416 -> 140363291013696
+	140363291009024 [label="Is feature volatile acidity(f_id: 1) <= 0.16?
+volatile acidity
+Treshold: 0.16"]
+	140363291013696 -> 140363291009024
+	140363291008896 [label="Class: 6"]
+	140363291009024 -> 140363291008896
+	140363291015104 [label="Is feature free sulfur dioxide(f_id: 5) <= 36.0?
+free sulfur dioxide
+Treshold: 36.0"]
+	140363291009024 -> 140363291015104
+	140363291013312 [label="Class: 6"]
+	140363291015104 -> 140363291013312
+	140363291008768 [label="Class: 5"]
+	140363291015104 -> 140363291008768
+	140363291013120 [label="Is feature density(f_id: 7) <= 0.9939?
+density
+Treshold: 0.9939"]
+	140363291013696 -> 140363291013120
+	140363291016832 [label="Is feature chlorides(f_id: 4) <= 0.031?
+chlorides
+Treshold: 0.031"]
+	140363291013120 -> 140363291016832
+	140363291014336 [label="Is feature sulphates(f_id: 9) <= 0.54?
+sulphates
+Treshold: 0.54"]
+	140363291016832 -> 140363291014336
+	140363291006272 [label="Class: 7"]
+	140363291014336 -> 140363291006272
+	140363291016256 [label="Class: 6"]
+	140363291014336 -> 140363291016256
+	140363291005824 [label="Is feature fixed acidity(f_id: 0) <= 7.1?
+fixed acidity
+Treshold: 7.1"]
+	140363291016832 -> 140363291005824
+	140363291009472 [label="Class: 7"]
+	140363291005824 -> 140363291009472
+	140363291007936 [label="Is feature alcohol(f_id: 10) <= 10.5?
+alcohol
+Treshold: 10.5"]
+	140363291005824 -> 140363291007936
+	140363291016448 [label="Is feature sulphates(f_id: 9) <= 0.68?
+sulphates
+Treshold: 0.68"]
+	140363291007936 -> 140363291016448
+	140363291002816 [label="Class: 7"]
+	140363291016448 -> 140363291002816
+	140363291004544 [label="Class: 6"]
+	140363291016448 -> 140363291004544
+	140363291004928 [label="Class: 6"]
+	140363291007936 -> 140363291004928
+	140363291016320 [label="Is feature residual sugar(f_id: 3) <= 1.8?
+residual sugar
+Treshold: 1.8"]
+	140363291013120 -> 140363291016320
+	140363291015936 [label="Class: 6"]
+	140363291016320 -> 140363291015936
+	140363291001856 [label="Is feature fixed acidity(f_id: 0) <= 6.8?
+fixed acidity
+Treshold: 6.8"]
+	140363291016320 -> 140363291001856
+	140363291007232 [label="Class: 6"]
+	140363291001856 -> 140363291007232
+	140363291005568 [label="Class: 7"]
+	140363291001856 -> 140363291005568
+	140363291004160 [label="Is feature alcohol(f_id: 10) <= 9.1?
+alcohol
+Treshold: 9.1"]
+	140363290722432 -> 140363291004160
+	140363291006784 [label="Is feature citric acid(f_id: 2) <= 0.31?
+citric acid
+Treshold: 0.31"]
+	140363291004160 -> 140363291006784
+	140363291011776 [label="Is feature citric acid(f_id: 2) <= 0.27?
+citric acid
+Treshold: 0.27"]
+	140363291006784 -> 140363291011776
+	140363291016128 [label="Class: 6"]
+	140363291011776 -> 140363291016128
+	140363291001664 [label="Is feature density(f_id: 7) <= 0.99971?
+density
+Treshold: 0.99971"]
+	140363291011776 -> 140363291001664
+	140363291008256 [label="Class: 7"]
+	140363291001664 -> 140363291008256
+	140363291006208 [label="Class: 6"]
+	140363291001664 -> 140363291006208
+	140363291015616 [label="Is feature volatile acidity(f_id: 1) <= 0.18?
+volatile acidity
+Treshold: 0.18"]
+	140363291006784 -> 140363291015616
+	140363291016000 [label="Is feature chlorides(f_id: 4) <= 0.05?
+chlorides
+Treshold: 0.05"]
+	140363291015616 -> 140363291016000
+	140363291004480 [label="Class: 6"]
+	140363291016000 -> 140363291004480
+	140363291011520 [label="Is feature citric acid(f_id: 2) <= 0.36?
+citric acid
+Treshold: 0.36"]
+	140363291016000 -> 140363291011520
+	140363291006848 [label="Class: 6"]
+	140363291011520 -> 140363291006848
+	140363291011072 [label="Class: 5"]
+	140363291011520 -> 140363291011072
+	140363291005696 [label="Is feature total sulfur dioxide(f_id: 6) <= 133.0?
+total sulfur dioxide
+Treshold: 133.0"]
+	140363291015616 -> 140363291005696
+	140363291012608 [label="Class: 5"]
+	140363291005696 -> 140363291012608
+	140363291004352 [label="Is feature residual sugar(f_id: 3) <= 15.55?
+residual sugar
+Treshold: 15.55"]
+	140363291005696 -> 140363291004352
+	140363291004608 [label="Class: 7"]
+	140363291004352 -> 140363291004608
+	140363291012544 [label="Class: 6"]
+	140363291004352 -> 140363291012544
+	140363291006912 [label="Is feature chlorides(f_id: 4) <= 0.043?
+chlorides
+Treshold: 0.043"]
+	140363291004160 -> 140363291006912
+	140363291001408 [label="Is feature free sulfur dioxide(f_id: 5) <= 40.0?
+free sulfur dioxide
+Treshold: 40.0"]
+	140363291006912 -> 140363291001408
+	140363291015424 [label="Is feature chlorides(f_id: 4) <= 0.039?
+chlorides
+Treshold: 0.039"]
+	140363291001408 -> 140363291015424
+	140363291006016 [label="Is feature pH(f_id: 8) <= 3.19?
+pH
+Treshold: 3.19"]
+	140363291015424 -> 140363291006016
+	140363291016960 [label="Is feature citric acid(f_id: 2) <= 0.31?
+citric acid
+Treshold: 0.31"]
+	140363291006016 -> 140363291016960
+	140363291015808 [label="Is feature alcohol(f_id: 10) <= 10.6?
+alcohol
+Treshold: 10.6"]
+	140363291016960 -> 140363291015808
+	140363291016896 [label="Class: 7"]
+	140363291015808 -> 140363291016896
+	140363291011264 [label="Class: 5"]
+	140363291015808 -> 140363291011264
+	140363290809856 [label="Class: 5"]
+	140363291016960 -> 140363290809856
+	140363290809280 [label="Is feature sulphates(f_id: 9) <= 0.49?
+sulphates
+Treshold: 0.49"]
+	140363291006016 -> 140363290809280
+	140363292878272 [label="Is feature density(f_id: 7) <= 0.99512?
+density
+Treshold: 0.99512"]
+	140363290809280 -> 140363292878272
+	140363292457472 [label="Class: 6"]
+	140363292878272 -> 140363292457472
+	140363291685504 [label="Class: 7"]
+	140363292878272 -> 140363291685504
+	140363291686656 [label="Is feature total sulfur dioxide(f_id: 6) <= 111.0?
+total sulfur dioxide
+Treshold: 111.0"]
+	140363290809280 -> 140363291686656
+	140363290810304 [label="Class: 7"]
+	140363291686656 -> 140363290810304
+	140363290819968 [label="Class: 5"]
+	140363291686656 -> 140363290819968
+	140363290807104 [label="Is feature total sulfur dioxide(f_id: 6) <= 124.0?
+total sulfur dioxide
+Treshold: 124.0"]
+	140363291015424 -> 140363290807104
+	140363290809536 [label="Class: 6"]
+	140363290807104 -> 140363290809536
+	140363290810240 [label="Class: 3"]
+	140363290807104 -> 140363290810240
+	140363290816192 [label="Is feature free sulfur dioxide(f_id: 5) <= 47.0?
+free sulfur dioxide
+Treshold: 47.0"]
+	140363291001408 -> 140363290816192
+	140363290807040 [label="Is feature total sulfur dioxide(f_id: 6) <= 98.0?
+total sulfur dioxide
+Treshold: 98.0"]
+	140363290816192 -> 140363290807040
+	140363290806912 [label="Class: 8"]
+	140363290807040 -> 140363290806912
+	140363290806528 [label="Is feature citric acid(f_id: 2) <= 0.26?
+citric acid
+Treshold: 0.26"]
+	140363290807040 -> 140363290806528
+	140363290820096 [label="Class: 6"]
+	140363290806528 -> 140363290820096
+	140363290817984 [label="Class: 7"]
+	140363290806528 -> 140363290817984
+	140363290806464 [label="Is feature fixed acidity(f_id: 0) <= 6.1?
+fixed acidity
+Treshold: 6.1"]
+	140363290816192 -> 140363290806464
+	140363290817792 [label="Is feature free sulfur dioxide(f_id: 5) <= 49.0?
+free sulfur dioxide
+Treshold: 49.0"]
+	140363290806464 -> 140363290817792
+	140363290809984 [label="Class: 8"]
+	140363290817792 -> 140363290809984
+	140363290809152 [label="Is feature density(f_id: 7) <= 0.9945?
+density
+Treshold: 0.9945"]
+	140363290817792 -> 140363290809152
+	140363290809600 [label="Class: 7"]
+	140363290809152 -> 140363290809600
+	140363290819904 [label="Class: 5"]
+	140363290809152 -> 140363290819904
+	140363290815616 [label="Is feature chlorides(f_id: 4) <= 0.042?
+chlorides
+Treshold: 0.042"]
+	140363290806464 -> 140363290815616
+	140363290813568 [label="Class: 6"]
+	140363290815616 -> 140363290813568
+	140363290815680 [label="Class: 7"]
+	140363290815616 -> 140363290815680
+	140363290819712 [label="Is feature citric acid(f_id: 2) <= 0.44?
+citric acid
+Treshold: 0.44"]
+	140363291006912 -> 140363290819712
+	140363290812352 [label="Is feature chlorides(f_id: 4) <= 0.119?
+chlorides
+Treshold: 0.119"]
+	140363290819712 -> 140363290812352
+	140363290812928 [label="Is feature density(f_id: 7) <= 0.99332?
+density
+Treshold: 0.99332"]
+	140363290812352 -> 140363290812928
+	140363290817408 [label="Class: 7"]
+	140363290812928 -> 140363290817408
+	140363290812032 [label="Is feature density(f_id: 7) <= 0.99666?
+density
+Treshold: 0.99666"]
+	140363290812928 -> 140363290812032
+	140363290805696 [label="Is feature alcohol(f_id: 10) <= 9.4?
+alcohol
+Treshold: 9.4"]
+	140363290812032 -> 140363290805696
+	140363290819392 [label="Is feature total sulfur dioxide(f_id: 6) <= 98.0?
+total sulfur dioxide
+Treshold: 98.0"]
+	140363290805696 -> 140363290819392
+	140363290804544 [label="Class: 6"]
+	140363290819392 -> 140363290804544
+	140363290819584 [label="Class: 5"]
+	140363290819392 -> 140363290819584
+	140363290809472 [label="Is feature total sulfur dioxide(f_id: 6) <= 242.0?
+total sulfur dioxide
+Treshold: 242.0"]
+	140363290805696 -> 140363290809472
+	140363290805312 [label="Is feature free sulfur dioxide(f_id: 5) <= 25.0?
+free sulfur dioxide
+Treshold: 25.0"]
+	140363290809472 -> 140363290805312
+	140363290815744 [label="Is feature volatile acidity(f_id: 1) <= 0.18?
+volatile acidity
+Treshold: 0.18"]
+	140363290805312 -> 140363290815744
+	140363290815168 [label="Is feature alcohol(f_id: 10) <= 9.6?
+alcohol
+Treshold: 9.6"]
+	140363290815744 -> 140363290815168
+	140363290806016 [label="Class: 5"]
+	140363290815168 -> 140363290806016
+	140363290810880 [label="Class: 6"]
+	140363290815168 -> 140363290810880
+	140363290808512 [label="Class: 5"]
+	140363290815744 -> 140363290808512
+	140363291034240 [label="Is feature total sulfur dioxide(f_id: 6) <= 95.0?
+total sulfur dioxide
+Treshold: 95.0"]
+	140363290805312 -> 140363291034240
+	140363291033792 [label="Is feature total sulfur dioxide(f_id: 6) <= 91.0?
+total sulfur dioxide
+Treshold: 91.0"]
+	140363291034240 -> 140363291033792
+	140363291033984 [label="Class: 6"]
+	140363291033792 -> 140363291033984
+	140363291034496 [label="Class: 7"]
+	140363291033792 -> 140363291034496
+	140363291034176 [label="Is feature density(f_id: 7) <= 0.99655?
+density
+Treshold: 0.99655"]
+	140363291034240 -> 140363291034176
+	140363291034112 [label="Is feature alcohol(f_id: 10) <= 10.7?
+alcohol
+Treshold: 10.7"]
+	140363291034176 -> 140363291034112
+	140363291034624 [label="Class: 6"]
+	140363291034112 -> 140363291034624
+	140363291034688 [label="Is feature chlorides(f_id: 4) <= 0.045?
+chlorides
+Treshold: 0.045"]
+	140363291034112 -> 140363291034688
+	140363291034816 [label="Class: 5"]
+	140363291034688 -> 140363291034816
+	140363291034880 [label="Class: 6"]
+	140363291034688 -> 140363291034880
+	140363291034944 [label="Is feature pH(f_id: 8) <= 3.11?
+pH
+Treshold: 3.11"]
+	140363291034176 -> 140363291034944
+	140363291035072 [label="Class: 6"]
+	140363291034944 -> 140363291035072
+	140363291035136 [label="Class: 5"]
+	140363291034944 -> 140363291035136
+	140363291035200 [label="Class: 5"]
+	140363290809472 -> 140363291035200
+	140363291035264 [label="Is feature fixed acidity(f_id: 0) <= 6.9?
+fixed acidity
+Treshold: 6.9"]
+	140363290812032 -> 140363291035264
+	140363291035392 [label="Is feature sulphates(f_id: 9) <= 0.37?
+sulphates
+Treshold: 0.37"]
+	140363291035264 -> 140363291035392
+	140363291035520 [label="Is feature free sulfur dioxide(f_id: 5) <= 53.0?
+free sulfur dioxide
+Treshold: 53.0"]
+	140363291035392 -> 140363291035520
+	140363291035584 [label="Class: 6"]
+	140363291035520 -> 140363291035584
+	140363291035648 [label="Class: 5"]
+	140363291035520 -> 140363291035648
+	140363291035712 [label="Class: 6"]
+	140363291035392 -> 140363291035712
+	140363291035776 [label="Is feature total sulfur dioxide(f_id: 6) <= 120.0?
+total sulfur dioxide
+Treshold: 120.0"]
+	140363291035264 -> 140363291035776
+	140363291035840 [label="Is feature density(f_id: 7) <= 0.99737?
+density
+Treshold: 0.99737"]
+	140363291035776 -> 140363291035840
+	140363291035968 [label="Class: 7"]
+	140363291035840 -> 140363291035968
+	140363291036032 [label="Class: 6"]
+	140363291035840 -> 140363291036032
+	140363291036096 [label="Is feature residual sugar(f_id: 3) <= 16.95?
+residual sugar
+Treshold: 16.95"]
+	140363291035776 -> 140363291036096
+	140363291036224 [label="Class: 6"]
+	140363291036096 -> 140363291036224
+	140363291036288 [label="Class: 7"]
+	140363291036096 -> 140363291036288
+	140363291036352 [label="Is feature density(f_id: 7) <= 0.99508?
+density
+Treshold: 0.99508"]
+	140363290812352 -> 140363291036352
+	140363291036480 [label="Class: 8"]
+	140363291036352 -> 140363291036480
+	140363291036544 [label="Class: 6"]
+	140363291036352 -> 140363291036544
+	140363291036608 [label="Is feature pH(f_id: 8) <= 2.91?
+pH
+Treshold: 2.91"]
+	140363290819712 -> 140363291036608
+	140363291036736 [label="Class: 4"]
+	140363291036608 -> 140363291036736
+	140363291036800 [label="Is feature density(f_id: 7) <= 0.998?
+density
+Treshold: 0.998"]
+	140363291036608 -> 140363291036800
+	140363291036928 [label="Is feature citric acid(f_id: 2) <= 0.45?
+citric acid
+Treshold: 0.45"]
+	140363291036800 -> 140363291036928
+	140363291037056 [label="Class: 5"]
+	140363291036928 -> 140363291037056
+	140363291037120 [label="Class: 6"]
+	140363291036928 -> 140363291037120
+	140363291037184 [label="Is feature volatile acidity(f_id: 1) <= 0.16?
+volatile acidity
+Treshold: 0.16"]
+	140363291036800 -> 140363291037184
+	140363291037248 [label="Class: 7"]
+	140363291037184 -> 140363291037248
+	140363291037312 [label="Class: 5"]
+	140363291037184 -> 140363291037312
+	140363291037376 [label="Is feature alcohol(f_id: 10) <= 9.73333333333333?
+alcohol
+Treshold: 9.73333333333333"]
+	140363292075136 -> 140363291037376
+	140363291037504 [label="Is feature fixed acidity(f_id: 0) <= 6.8?
+fixed acidity
+Treshold: 6.8"]
+	140363291037376 -> 140363291037504
+	140363291037632 [label="Is feature total sulfur dioxide(f_id: 6) <= 132.0?
+total sulfur dioxide
+Treshold: 132.0"]
+	140363291037504 -> 140363291037632
+	140363291037696 [label="Is feature residual sugar(f_id: 3) <= 5.1?
+residual sugar
+Treshold: 5.1"]
+	140363291037632 -> 140363291037696
+	140363291037824 [label="Is feature sulphates(f_id: 9) <= 0.38?
+sulphates
+Treshold: 0.38"]
+	140363291037696 -> 140363291037824
+	140363291037952 [label="Class: 6"]
+	140363291037824 -> 140363291037952
+	140363291038016 [label="Is feature citric acid(f_id: 2) <= 0.27?
+citric acid
+Treshold: 0.27"]
+	140363291037824 -> 140363291038016
+	140363291038144 [label="Is feature total sulfur dioxide(f_id: 6) <= 105.0?
+total sulfur dioxide
+Treshold: 105.0"]
+	140363291038016 -> 140363291038144
+	140363291038208 [label="Class: 5"]
+	140363291038144 -> 140363291038208
+	140363291038272 [label="Class: 6"]
+	140363291038144 -> 140363291038272
+	140363291038336 [label="Class: 5"]
+	140363291038016 -> 140363291038336
+	140363291038400 [label="Is feature chlorides(f_id: 4) <= 0.053?
+chlorides
+Treshold: 0.053"]
+	140363291037696 -> 140363291038400
+	140363291038528 [label="Class: 6"]
+	140363291038400 -> 140363291038528
+	140363291038592 [label="Class: 5"]
+	140363291038400 -> 140363291038592
+	140363291038656 [label="Is feature residual sugar(f_id: 3) <= 7.6?
+residual sugar
+Treshold: 7.6"]
+	140363291037632 -> 140363291038656
+	140363291038784 [label="Is feature pH(f_id: 8) <= 3.31?
+pH
+Treshold: 3.31"]
+	140363291038656 -> 140363291038784
+	140363291038912 [label="Is feature chlorides(f_id: 4) <= 0.079?
+chlorides
+Treshold: 0.079"]
+	140363291038784 -> 140363291038912
+	140363291039040 [label="Is feature alcohol(f_id: 10) <= 9.1?
+alcohol
+Treshold: 9.1"]
+	140363291038912 -> 140363291039040
+	140363291039168 [label="Class: 5"]
+	140363291039040 -> 140363291039168
+	140363291039232 [label="Is feature free sulfur dioxide(f_id: 5) <= 48.0?
+free sulfur dioxide
+Treshold: 48.0"]
+	140363291039040 -> 140363291039232
+	140363291039296 [label="Is feature pH(f_id: 8) <= 3.12?
+pH
+Treshold: 3.12"]
+	140363291039232 -> 140363291039296
+	140363291039424 [label="Is feature free sulfur dioxide(f_id: 5) <= 22.0?
+free sulfur dioxide
+Treshold: 22.0"]
+	140363291039296 -> 140363291039424
+	140363291039488 [label="Class: 5"]
+	140363291039424 -> 140363291039488
+	140363291039552 [label="Class: 6"]
+	140363291039424 -> 140363291039552
+	140363291039616 [label="Is feature free sulfur dioxide(f_id: 5) <= 33.0?
+free sulfur dioxide
+Treshold: 33.0"]
+	140363291039296 -> 140363291039616
+	140363291039680 [label="Is feature sulphates(f_id: 9) <= 0.42?
+sulphates
+Treshold: 0.42"]
+	140363291039616 -> 140363291039680
+	140363291039808 [label="Class: 5"]
+	140363291039680 -> 140363291039808
+	140363291039872 [label="Class: 6"]
+	140363291039680 -> 140363291039872
+	140363291039936 [label="Class: 5"]
+	140363291039616 -> 140363291039936
+	140363291040000 [label="Is feature sulphates(f_id: 9) <= 0.45?
+sulphates
+Treshold: 0.45"]
+	140363291039232 -> 140363291040000
+	140363291040128 [label="Is feature residual sugar(f_id: 3) <= 6.8?
+residual sugar
+Treshold: 6.8"]
+	140363291040000 -> 140363291040128
+	140363291040256 [label="Class: 6"]
+	140363291040128 -> 140363291040256
+	140363291040320 [label="Class: 5"]
+	140363291040128 -> 140363291040320
+	140363291040384 [label="Class: 6"]
+	140363291040000 -> 140363291040384
+	140363291040448 [label="Is feature residual sugar(f_id: 3) <= 1.1?
+residual sugar
+Treshold: 1.1"]
+	140363291038912 -> 140363291040448
+	140363291040576 [label="Class: 7"]
+	140363291040448 -> 140363291040576
+	140363291040640 [label="Class: 6"]
+	140363291040448 -> 140363291040640
+	140363291040704 [label="Is feature residual sugar(f_id: 3) <= 7.2?
+residual sugar
+Treshold: 7.2"]
+	140363291038784 -> 140363291040704
+	140363291040832 [label="Is feature alcohol(f_id: 10) <= 9.4?
+alcohol
+Treshold: 9.4"]
+	140363291040704 -> 140363291040832
+	140363291040960 [label="Class: 3"]
+	140363291040832 -> 140363291040960
+	140363291041024 [label="Is feature residual sugar(f_id: 3) <= 1.1?
+residual sugar
+Treshold: 1.1"]
+	140363291040832 -> 140363291041024
+	140363291041152 [label="Class: 8"]
+	140363291041024 -> 140363291041152
+	140363291041216 [label="Class: 6"]
+	140363291041024 -> 140363291041216
+	140363291041280 [label="Class: 7"]
+	140363291040704 -> 140363291041280
+	140363291041344 [label="Is feature total sulfur dioxide(f_id: 6) <= 139.0?
+total sulfur dioxide
+Treshold: 139.0"]
+	140363291038656 -> 140363291041344
+	140363291041408 [label="Class: 5"]
+	140363291041344 -> 140363291041408
+	140363291041472 [label="Is feature volatile acidity(f_id: 1) <= 0.23?
+volatile acidity
+Treshold: 0.23"]
+	140363291041344 -> 140363291041472
+	140363291041536 [label="Is feature volatile acidity(f_id: 1) <= 0.21?
+volatile acidity
+Treshold: 0.21"]
+	140363291041472 -> 140363291041536
+	140363291041600 [label="Is feature chlorides(f_id: 4) <= 0.044?
+chlorides
+Treshold: 0.044"]
+	140363291041536 -> 140363291041600
+	140363291041728 [label="Is feature fixed acidity(f_id: 0) <= 6.6?
+fixed acidity
+Treshold: 6.6"]
+	140363291041600 -> 140363291041728
+	140363291041856 [label="Is feature total sulfur dioxide(f_id: 6) <= 158.0?
+total sulfur dioxide
+Treshold: 158.0"]
+	140363291041728 -> 140363291041856
+	140363291041920 [label="Class: 6"]
+	140363291041856 -> 140363291041920
+	140363291041984 [label="Class: 5"]
+	140363291041856 -> 140363291041984
+	140363291042048 [label="Class: 5"]
+	140363291041728 -> 140363291042048
+	140363291042112 [label="Class: 5"]
+	140363291041600 -> 140363291042112
+	140363291042176 [label="Is feature pH(f_id: 8) <= 3.05?
+pH
+Treshold: 3.05"]
+	140363291041536 -> 140363291042176
+	140363291042304 [label="Is feature sulphates(f_id: 9) <= 0.4?
+sulphates
+Treshold: 0.4"]
+	140363291042176 -> 140363291042304
+	140363291042432 [label="Class: 5"]
+	140363291042304 -> 140363291042432
+	140363291042496 [label="Is feature chlorides(f_id: 4) <= 0.07?
+chlorides
+Treshold: 0.07"]
+	140363291042304 -> 140363291042496
+	140363291042624 [label="Class: 6"]
+	140363291042496 -> 140363291042624
+	140363291042688 [label="Class: 5"]
+	140363291042496 -> 140363291042688
+	140363291042752 [label="Is feature total sulfur dioxide(f_id: 6) <= 140.0?
+total sulfur dioxide
+Treshold: 140.0"]
+	140363291042176 -> 140363291042752
+	140363291042816 [label="Class: 5"]
+	140363291042752 -> 140363291042816
+	140363291042880 [label="Class: 6"]
+	140363291042752 -> 140363291042880
+	140363291042944 [label="Is feature free sulfur dioxide(f_id: 5) <= 30.0?
+free sulfur dioxide
+Treshold: 30.0"]
+	140363291041472 -> 140363291042944
+	140363291043008 [label="Class: 6"]
+	140363291042944 -> 140363291043008
+	140363291043072 [label="Is feature citric acid(f_id: 2) <= 0.56?
+citric acid
+Treshold: 0.56"]
+	140363291042944 -> 140363291043072
+	140363291043200 [label="Is feature free sulfur dioxide(f_id: 5) <= 64.0?
+free sulfur dioxide
+Treshold: 64.0"]
+	140363291043072 -> 140363291043200
+	140363291043264 [label="Is feature sulphates(f_id: 9) <= 0.35?
+sulphates
+Treshold: 0.35"]
+	140363291043200 -> 140363291043264
+	140363291043392 [label="Class: 6"]
+	140363291043264 -> 140363291043392
+	140363291043456 [label="Is feature sulphates(f_id: 9) <= 0.45?
+sulphates
+Treshold: 0.45"]
+	140363291043264 -> 140363291043456
+	140363291043584 [label="Class: 5"]
+	140363291043456 -> 140363291043584
+	140363291043648 [label="Is feature fixed acidity(f_id: 0) <= 6.1?
+fixed acidity
+Treshold: 6.1"]
+	140363291043456 -> 140363291043648
+	140363291043776 [label="Class: 6"]
+	140363291043648 -> 140363291043776
+	140363291043840 [label="Is feature sulphates(f_id: 9) <= 0.47?
+sulphates
+Treshold: 0.47"]
+	140363291043648 -> 140363291043840
+	140363291043968 [label="Is feature free sulfur dioxide(f_id: 5) <= 53.0?
+free sulfur dioxide
+Treshold: 53.0"]
+	140363291043840 -> 140363291043968
+	140363291044032 [label="Is feature pH(f_id: 8) <= 3.16?
+pH
+Treshold: 3.16"]
+	140363291043968 -> 140363291044032
+	140363291044160 [label="Class: 6"]
+	140363291044032 -> 140363291044160
+	140363291044224 [label="Is feature free sulfur dioxide(f_id: 5) <= 44.0?
+free sulfur dioxide
+Treshold: 44.0"]
+	140363291044032 -> 140363291044224
+	140363291044288 [label="Class: 6"]
+	140363291044224 -> 140363291044288
+	140363291044352 [label="Class: 5"]
+	140363291044224 -> 140363291044352
+	140363291044416 [label="Class: 5"]
+	140363291043968 -> 140363291044416
+	140363291044480 [label="Is feature pH(f_id: 8) <= 3.15?
+pH
+Treshold: 3.15"]
+	140363291043840 -> 140363291044480
+	140363291044608 [label="Is feature chlorides(f_id: 4) <= 0.045?
+chlorides
+Treshold: 0.045"]
+	140363291044480 -> 140363291044608
+	140363291044736 [label="Class: 6"]
+	140363291044608 -> 140363291044736
+	140363291044800 [label="Is feature alcohol(f_id: 10) <= 9.1?
+alcohol
+Treshold: 9.1"]
+	140363291044608 -> 140363291044800
+	140363291044928 [label="Is feature free sulfur dioxide(f_id: 5) <= 40.0?
+free sulfur dioxide
+Treshold: 40.0"]
+	140363291044800 -> 140363291044928
+	140363291044992 [label="Class: 6"]
+	140363291044928 -> 140363291044992
+	140363291045056 [label="Class: 5"]
+	140363291044928 -> 140363291045056
+	140363291045120 [label="Class: 5"]
+	140363291044800 -> 140363291045120
+	140363291045184 [label="Class: 5"]
+	140363291044480 -> 140363291045184
+	140363291045248 [label="Class: 6"]
+	140363291043200 -> 140363291045248
+	140363291045312 [label="Class: 6"]
+	140363291043072 -> 140363291045312
+	140363291045376 [label="Is feature density(f_id: 7) <= 0.9984?
+density
+Treshold: 0.9984"]
+	140363291037504 -> 140363291045376
+	140363291045504 [label="Is feature density(f_id: 7) <= 0.9965?
+density
+Treshold: 0.9965"]
+	140363291045376 -> 140363291045504
+	140363291045632 [label="Is feature fixed acidity(f_id: 0) <= 6.9?
+fixed acidity
+Treshold: 6.9"]
+	140363291045504 -> 140363291045632
+	140363291045760 [label="Is feature volatile acidity(f_id: 1) <= 0.23?
+volatile acidity
+Treshold: 0.23"]
+	140363291045632 -> 140363291045760
+	140363291045824 [label="Is feature pH(f_id: 8) <= 3.02?
+pH
+Treshold: 3.02"]
+	140363291045760 -> 140363291045824
+	140363291045952 [label="Class: 8"]
+	140363291045824 -> 140363291045952
+	140363291046016 [label="Class: 6"]
+	140363291045824 -> 140363291046016
+	140363291046080 [label="Class: 4"]
+	140363291045760 -> 140363291046080
+	140363291046144 [label="Is feature chlorides(f_id: 4) <= 0.047?
+chlorides
+Treshold: 0.047"]
+	140363291045632 -> 140363291046144
+	140363291046272 [label="Is feature density(f_id: 7) <= 0.9962?
+density
+Treshold: 0.9962"]
+	140363291046144 -> 140363291046272
+	140363291046400 [label="Is feature free sulfur dioxide(f_id: 5) <= 17.0?
+free sulfur dioxide
+Treshold: 17.0"]
+	140363291046272 -> 140363291046400
+	140363291046464 [label="Class: 5"]
+	140363291046400 -> 140363291046464
+	140363291046528 [label="Is feature free sulfur dioxide(f_id: 5) <= 52.0?
+free sulfur dioxide
+Treshold: 52.0"]
+	140363291046400 -> 140363291046528
+	140363291046592 [label="Class: 6"]
+	140363291046528 -> 140363291046592
+	140363291046656 [label="Class: 5"]
+	140363291046528 -> 140363291046656
+	140363291046720 [label="Class: 5"]
+	140363291046272 -> 140363291046720
+	140363291046784 [label="Is feature volatile acidity(f_id: 1) <= 0.23?
+volatile acidity
+Treshold: 0.23"]
+	140363291046144 -> 140363291046784
+	140363291046848 [label="Is feature fixed acidity(f_id: 0) <= 7.8?
+fixed acidity
+Treshold: 7.8"]
+	140363291046784 -> 140363291046848
+	140363291046976 [label="Is feature density(f_id: 7) <= 0.9931?
+density
+Treshold: 0.9931"]
+	140363291046848 -> 140363291046976
+	140363291047104 [label="Class: 6"]
+	140363291046976 -> 140363291047104
+	140363291047168 [label="Class: 5"]
+	140363291046976 -> 140363291047168
+	140363291047232 [label="Is feature sulphates(f_id: 9) <= 0.46?
+sulphates
+Treshold: 0.46"]
+	140363291046848 -> 140363291047232
+	140363291047360 [label="Class: 4"]
+	140363291047232 -> 140363291047360
+	140363291047424 [label="Class: 6"]
+	140363291047232 -> 140363291047424
+	140363291047488 [label="Class: 5"]
+	140363291046784 -> 140363291047488
+	140363291047552 [label="Is feature alcohol(f_id: 10) <= 8.8?
+alcohol
+Treshold: 8.8"]
+	140363291045504 -> 140363291047552
+	140363291047680 [label="Class: 5"]
+	140363291047552 -> 140363291047680
+	140363291047744 [label="Is feature pH(f_id: 8) <= 3.09?
+pH
+Treshold: 3.09"]
+	140363291047552 -> 140363291047744
+	140363291047872 [label="Is feature total sulfur dioxide(f_id: 6) <= 181.0?
+total sulfur dioxide
+Treshold: 181.0"]
+	140363291047744 -> 140363291047872
+	140363291047936 [label="Class: 6"]
+	140363291047872 -> 140363291047936
+	140363291048000 [label="Is feature pH(f_id: 8) <= 2.93?
+pH
+Treshold: 2.93"]
+	140363291047872 -> 140363291048000
+	140363291048128 [label="Class: 6"]
+	140363291048000 -> 140363291048128
+	140363291048192 [label="Is feature free sulfur dioxide(f_id: 5) <= 53.0?
+free sulfur dioxide
+Treshold: 53.0"]
+	140363291048000 -> 140363291048192
+	140363291048256 [label="Is feature sulphates(f_id: 9) <= 0.44?
+sulphates
+Treshold: 0.44"]
+	140363291048192 -> 140363291048256
+	140363291048384 [label="Class: 5"]
+	140363291048256 -> 140363291048384
+	140363291048448 [label="Class: 7"]
+	140363291048256 -> 140363291048448
+	140363291048512 [label="Is feature pH(f_id: 8) <= 2.95?
+pH
+Treshold: 2.95"]
+	140363291048192 -> 140363291048512
+	140363291048640 [label="Class: 5"]
+	140363291048512 -> 140363291048640
+	140363291048704 [label="Class: 6"]
+	140363291048512 -> 140363291048704
+	140363291048768 [label="Is feature density(f_id: 7) <= 0.99708?
+density
+Treshold: 0.99708"]
+	140363291047744 -> 140363291048768
+	140363291048896 [label="Class: 6"]
+	140363291048768 -> 140363291048896
+	140363291048960 [label="Is feature sulphates(f_id: 9) <= 0.59?
+sulphates
+Treshold: 0.59"]
+	140363291048768 -> 140363291048960
+	140363291049088 [label="Class: 5"]
+	140363291048960 -> 140363291049088
+	140363291049152 [label="Class: 6"]
+	140363291048960 -> 140363291049152
+	140363291049216 [label="Is feature volatile acidity(f_id: 1) <= 0.24?
+volatile acidity
+Treshold: 0.24"]
+	140363291045376 -> 140363291049216
+	140363291049280 [label="Is feature residual sugar(f_id: 3) <= 17.45?
+residual sugar
+Treshold: 17.45"]
+	140363291049216 -> 140363291049280
+	140363291049408 [label="Is feature total sulfur dioxide(f_id: 6) <= 178.0?
+total sulfur dioxide
+Treshold: 178.0"]
+	140363291049280 -> 140363291049408
+	140363291049472 [label="Is feature alcohol(f_id: 10) <= 9.6?
+alcohol
+Treshold: 9.6"]
+	140363291049408 -> 140363291049472
+	140363291049600 [label="Is feature residual sugar(f_id: 3) <= 15.0?
+residual sugar
+Treshold: 15.0"]
+	140363291049472 -> 140363291049600
+	140363291049728 [label="Is feature pH(f_id: 8) <= 3.03?
+pH
+Treshold: 3.03"]
+	140363291049600 -> 140363291049728
+	140363291049856 [label="Class: 6"]
+	140363291049728 -> 140363291049856
+	140363291049920 [label="Class: 7"]
+	140363291049728 -> 140363291049920
+	140363291197504 [label="Class: 6"]
+	140363291049600 -> 140363291197504
+	140363291197568 [label="Is feature pH(f_id: 8) <= 2.93?
+pH
+Treshold: 2.93"]
+	140363291049472 -> 140363291197568
+	140363291197696 [label="Is feature total sulfur dioxide(f_id: 6) <= 123.0?
+total sulfur dioxide
+Treshold: 123.0"]
+	140363291197568 -> 140363291197696
+	140363291197760 [label="Class: 3"]
+	140363291197696 -> 140363291197760
+	140363291197824 [label="Class: 6"]
+	140363291197696 -> 140363291197824
+	140363291197888 [label="Class: 5"]
+	140363291197568 -> 140363291197888
+	140363291197952 [label="Is feature fixed acidity(f_id: 0) <= 7.2?
+fixed acidity
+Treshold: 7.2"]
+	140363291049408 -> 140363291197952
+	140363291198080 [label="Class: 5"]
+	140363291197952 -> 140363291198080
+	140363291198144 [label="Is feature total sulfur dioxide(f_id: 6) <= 191.0?
+total sulfur dioxide
+Treshold: 191.0"]
+	140363291197952 -> 140363291198144
+	140363291198208 [label="Class: 6"]
+	140363291198144 -> 140363291198208
+	140363291198272 [label="Is feature free sulfur dioxide(f_id: 5) <= 70.0?
+free sulfur dioxide
+Treshold: 70.0"]
+	140363291198144 -> 140363291198272
+	140363291198336 [label="Class: 5"]
+	140363291198272 -> 140363291198336
+	140363291198400 [label="Class: 6"]
+	140363291198272 -> 140363291198400
+	140363291198464 [label="Class: 5"]
+	140363291049280 -> 140363291198464
+	140363291198528 [label="Is feature fixed acidity(f_id: 0) <= 7.3?
+fixed acidity
+Treshold: 7.3"]
+	140363291049216 -> 140363291198528
+	140363291198656 [label="Is feature sulphates(f_id: 9) <= 0.35?
+sulphates
+Treshold: 0.35"]
+	140363291198528 -> 140363291198656
+	140363291198784 [label="Class: 6"]
+	140363291198656 -> 140363291198784
+	140363291198848 [label="Class: 7"]
+	140363291198656 -> 140363291198848
+	140363291198912 [label="Class: 5"]
+	140363291198528 -> 140363291198912
+	140363291198976 [label="Is feature free sulfur dioxide(f_id: 5) <= 23.0?
+free sulfur dioxide
+Treshold: 23.0"]
+	140363291037376 -> 140363291198976
+	140363291199040 [label="Is feature sulphates(f_id: 9) <= 0.59?
+sulphates
+Treshold: 0.59"]
+	140363291198976 -> 140363291199040
+	140363291199168 [label="Is feature volatile acidity(f_id: 1) <= 0.25?
+volatile acidity
+Treshold: 0.25"]
+	140363291199040 -> 140363291199168
+	140363291199232 [label="Is feature pH(f_id: 8) <= 3.24?
+pH
+Treshold: 3.24"]
+	140363291199168 -> 140363291199232
+	140363291199360 [label="Is feature residual sugar(f_id: 3) <= 0.7?
+residual sugar
+Treshold: 0.7"]
+	140363291199232 -> 140363291199360
+	140363291199488 [label="Is feature density(f_id: 7) <= 0.9918?
+density
+Treshold: 0.9918"]
+	140363291199360 -> 140363291199488
+	140363291199616 [label="Class: 4"]
+	140363291199488 -> 140363291199616
+	140363291199680 [label="Class: 5"]
+	140363291199488 -> 140363291199680
+	140363291199744 [label="Is feature citric acid(f_id: 2) <= 0.28?
+citric acid
+Treshold: 0.28"]
+	140363291199360 -> 140363291199744
+	140363291199872 [label="Is feature fixed acidity(f_id: 0) <= 8.6?
+fixed acidity
+Treshold: 8.6"]
+	140363291199744 -> 140363291199872
+	140363291200000 [label="Class: 5"]
+	140363291199872 -> 140363291200000
+	140363291200064 [label="Is feature fixed acidity(f_id: 0) <= 8.8?
+fixed acidity
+Treshold: 8.8"]
+	140363291199872 -> 140363291200064
+	140363291200192 [label="Class: 6"]
+	140363291200064 -> 140363291200192
+	140363291200256 [label="Class: 5"]
+	140363291200064 -> 140363291200256
+	140363291200320 [label="Is feature alcohol(f_id: 10) <= 10.6?
+alcohol
+Treshold: 10.6"]
+	140363291199744 -> 140363291200320
+	140363291200448 [label="Is feature chlorides(f_id: 4) <= 0.038?
+chlorides
+Treshold: 0.038"]
+	140363291200320 -> 140363291200448
+	140363291200576 [label="Class: 6"]
+	140363291200448 -> 140363291200576
+	140363291200640 [label="Is feature chlorides(f_id: 4) <= 0.041?
+chlorides
+Treshold: 0.041"]
+	140363291200448 -> 140363291200640
+	140363291200768 [label="Class: 5"]
+	140363291200640 -> 140363291200768
+	140363291200832 [label="Is feature volatile acidity(f_id: 1) <= 0.22?
+volatile acidity
+Treshold: 0.22"]
+	140363291200640 -> 140363291200832
+	140363291200896 [label="Class: 6"]
+	140363291200832 -> 140363291200896
+	140363291200960 [label="Is feature fixed acidity(f_id: 0) <= 7.8?
+fixed acidity
+Treshold: 7.8"]
+	140363291200832 -> 140363291200960
+	140363291201088 [label="Class: 5"]
+	140363291200960 -> 140363291201088
+	140363291201152 [label="Is feature total sulfur dioxide(f_id: 6) <= 106.0?
+total sulfur dioxide
+Treshold: 106.0"]
+	140363291200960 -> 140363291201152
+	140363291201216 [label="Class: 5"]
+	140363291201152 -> 140363291201216
+	140363291201280 [label="Class: 6"]
+	140363291201152 -> 140363291201280
+	140363291201344 [label="Is feature fixed acidity(f_id: 0) <= 7.4?
+fixed acidity
+Treshold: 7.4"]
+	140363291200320 -> 140363291201344
+	140363291201472 [label="Class: 7"]
+	140363291201344 -> 140363291201472
+	140363291201536 [label="Is feature density(f_id: 7) <= 0.9926?
+density
+Treshold: 0.9926"]
+	140363291201344 -> 140363291201536
+	140363291201664 [label="Class: 6"]
+	140363291201536 -> 140363291201664
+	140363291201728 [label="Class: 5"]
+	140363291201536 -> 140363291201728
+	140363291201792 [label="Is feature alcohol(f_id: 10) <= 10.1?
+alcohol
+Treshold: 10.1"]
+	140363291199232 -> 140363291201792
+	140363291201920 [label="Is feature sulphates(f_id: 9) <= 0.47?
+sulphates
+Treshold: 0.47"]
+	140363291201792 -> 140363291201920
+	140363291202048 [label="Class: 4"]
+	140363291201920 -> 140363291202048
+	140363291202112 [label="Is feature fixed acidity(f_id: 0) <= 5.0?
+fixed acidity
+Treshold: 5.0"]
+	140363291201920 -> 140363291202112
+	140363291202240 [label="Class: 5"]
+	140363291202112 -> 140363291202240
+	140363291202304 [label="Class: 6"]
+	140363291202112 -> 140363291202304
+	140363291202368 [label="Is feature citric acid(f_id: 2) <= 0.34?
+citric acid
+Treshold: 0.34"]
+	140363291201792 -> 140363291202368
+	140363291202496 [label="Class: 6"]
+	140363291202368 -> 140363291202496
+	140363291202560 [label="Is feature pH(f_id: 8) <= 3.31?
+pH
+Treshold: 3.31"]
+	140363291202368 -> 140363291202560
+	140363291202688 [label="Class: 7"]
+	140363291202560 -> 140363291202688
+	140363291202752 [label="Class: 6"]
+	140363291202560 -> 140363291202752
+	140363291202816 [label="Is feature fixed acidity(f_id: 0) <= 7.8?
+fixed acidity
+Treshold: 7.8"]
+	140363291199168 -> 140363291202816
+	140363291202944 [label="Is feature citric acid(f_id: 2) <= 0.18?
+citric acid
+Treshold: 0.18"]
+	140363291202816 -> 140363291202944
+	140363291203072 [label="Class: 6"]
+	140363291202944 -> 140363291203072
+	140363291203136 [label="Class: 5"]
+	140363291202944 -> 140363291203136
+	140363291203200 [label="Class: 6"]
+	140363291202816 -> 140363291203200
+	140363291203264 [label="Is feature total sulfur dioxide(f_id: 6) <= 111.0?
+total sulfur dioxide
+Treshold: 111.0"]
+	140363291199040 -> 140363291203264
+	140363291203328 [label="Is feature fixed acidity(f_id: 0) <= 6.8?
+fixed acidity
+Treshold: 6.8"]
+	140363291203264 -> 140363291203328
+	140363291203456 [label="Is feature chlorides(f_id: 4) <= 0.042?
+chlorides
+Treshold: 0.042"]
+	140363291203328 -> 140363291203456
+	140363291203584 [label="Class: 6"]
+	140363291203456 -> 140363291203584
+	140363291203648 [label="Class: 8"]
+	140363291203456 -> 140363291203648
+	140363291203712 [label="Class: 4"]
+	140363291203328 -> 140363291203712
+	140363291203776 [label="Is feature citric acid(f_id: 2) <= 0.43?
+citric acid
+Treshold: 0.43"]
+	140363291203264 -> 140363291203776
+	140363291203904 [label="Class: 6"]
+	140363291203776 -> 140363291203904
+	140363291203968 [label="Class: 5"]
+	140363291203776 -> 140363291203968
+	140363291204032 [label="Is feature sulphates(f_id: 9) <= 0.42?
+sulphates
+Treshold: 0.42"]
+	140363291198976 -> 140363291204032
+	140363291204160 [label="Is feature free sulfur dioxide(f_id: 5) <= 31.0?
+free sulfur dioxide
+Treshold: 31.0"]
+	140363291204032 -> 140363291204160
+	140363291204224 [label="Is feature free sulfur dioxide(f_id: 5) <= 26.0?
+free sulfur dioxide
+Treshold: 26.0"]
+	140363291204160 -> 140363291204224
+	140363291204288 [label="Is feature free sulfur dioxide(f_id: 5) <= 25.0?
+free sulfur dioxide
+Treshold: 25.0"]
+	140363291204224 -> 140363291204288
+	140363291204352 [label="Class: 6"]
+	140363291204288 -> 140363291204352
+	140363291204416 [label="Class: 5"]
+	140363291204288 -> 140363291204416
+	140363291204480 [label="Is feature total sulfur dioxide(f_id: 6) <= 108.0?
+total sulfur dioxide
+Treshold: 108.0"]
+	140363291204224 -> 140363291204480
+	140363291204544 [label="Is feature density(f_id: 7) <= 0.99486?
+density
+Treshold: 0.99486"]
+	140363291204480 -> 140363291204544
+	140363291204672 [label="Class: 6"]
+	140363291204544 -> 140363291204672
+	140363291204736 [label="Class: 7"]
+	140363291204544 -> 140363291204736
+	140363291204800 [label="Class: 7"]
+	140363291204480 -> 140363291204800
+	140363291204864 [label="Is feature chlorides(f_id: 4) <= 0.021?
+chlorides
+Treshold: 0.021"]
+	140363291204160 -> 140363291204864
+	140363291204992 [label="Class: 7"]
+	140363291204864 -> 140363291204992
+	140363291205056 [label="Is feature citric acid(f_id: 2) <= 0.37?
+citric acid
+Treshold: 0.37"]
+	140363291204864 -> 140363291205056
+	140363291205184 [label="Is feature residual sugar(f_id: 3) <= 14.7?
+residual sugar
+Treshold: 14.7"]
+	140363291205056 -> 140363291205184
+	140363291205312 [label="Is feature pH(f_id: 8) <= 3.17?
+pH
+Treshold: 3.17"]
+	140363291205184 -> 140363291205312
+	140363291205440 [label="Is feature density(f_id: 7) <= 0.9928?
+density
+Treshold: 0.9928"]
+	140363291205312 -> 140363291205440
+	140363291205568 [label="Class: 5"]
+	140363291205440 -> 140363291205568
+	140363291205632 [label="Is feature citric acid(f_id: 2) <= 0.18?
+citric acid
+Treshold: 0.18"]
+	140363291205440 -> 140363291205632
+	140363291205760 [label="Class: 5"]
+	140363291205632 -> 140363291205760
+	140363291205824 [label="Class: 6"]
+	140363291205632 -> 140363291205824
+	140363291205888 [label="Is feature residual sugar(f_id: 3) <= 5.6?
+residual sugar
+Treshold: 5.6"]
+	140363291205312 -> 140363291205888
+	140363291206016 [label="Is feature pH(f_id: 8) <= 3.2?
+pH
+Treshold: 3.2"]
+	140363291205888 -> 140363291206016
+	140363291206144 [label="Class: 5"]
+	140363291206016 -> 140363291206144
+	140363291206208 [label="Class: 6"]
+	140363291206016 -> 140363291206208
+	140363291206272 [label="Is feature chlorides(f_id: 4) <= 0.055?
+chlorides
+Treshold: 0.055"]
+	140363291205888 -> 140363291206272
+	140363291206400 [label="Is feature citric acid(f_id: 2) <= 0.28?
+citric acid
+Treshold: 0.28"]
+	140363291206272 -> 140363291206400
+	140363291206528 [label="Is feature citric acid(f_id: 2) <= 0.25?
+citric acid
+Treshold: 0.25"]
+	140363291206400 -> 140363291206528
+	140363291206656 [label="Class: 5"]
+	140363291206528 -> 140363291206656
+	140363291206720 [label="Class: 6"]
+	140363291206528 -> 140363291206720
+	140363291206784 [label="Class: 5"]
+	140363291206400 -> 140363291206784
+	140363291206848 [label="Class: 6"]
+	140363291206272 -> 140363291206848
+	140363291206912 [label="Class: 5"]
+	140363291205184 -> 140363291206912
+	140363291206976 [label="Class: 6"]
+	140363291205056 -> 140363291206976
+	140363291207040 [label="Is feature total sulfur dioxide(f_id: 6) <= 172.0?
+total sulfur dioxide
+Treshold: 172.0"]
+	140363291204032 -> 140363291207040
+	140363291207104 [label="Is feature chlorides(f_id: 4) <= 0.038?
+chlorides
+Treshold: 0.038"]
+	140363291207040 -> 140363291207104
+	140363291207232 [label="Is feature total sulfur dioxide(f_id: 6) <= 85.0?
+total sulfur dioxide
+Treshold: 85.0"]
+	140363291207104 -> 140363291207232
+	140363291207296 [label="Class: 5"]
+	140363291207232 -> 140363291207296
+	140363291207360 [label="Is feature free sulfur dioxide(f_id: 5) <= 41.0?
+free sulfur dioxide
+Treshold: 41.0"]
+	140363291207232 -> 140363291207360
+	140363291207424 [label="Is feature residual sugar(f_id: 3) <= 5.2?
+residual sugar
+Treshold: 5.2"]
+	140363291207360 -> 140363291207424
+	140363291207552 [label="Is feature total sulfur dioxide(f_id: 6) <= 129.0?
+total sulfur dioxide
+Treshold: 129.0"]
+	140363291207424 -> 140363291207552
+	140363291207616 [label="Is feature density(f_id: 7) <= 0.9912?
+density
+Treshold: 0.9912"]
+	140363291207552 -> 140363291207616
+	140363291207744 [label="Is feature density(f_id: 7) <= 0.99074?
+density
+Treshold: 0.99074"]
+	140363291207616 -> 140363291207744
+	140363291207872 [label="Class: 6"]
+	140363291207744 -> 140363291207872
+	140363291207936 [label="Class: 7"]
+	140363291207744 -> 140363291207936
+	140363291208000 [label="Class: 6"]
+	140363291207616 -> 140363291208000
+	140363291208064 [label="Is feature citric acid(f_id: 2) <= 0.25?
+citric acid
+Treshold: 0.25"]
+	140363291207552 -> 140363291208064
+	140363291208192 [label="Class: 6"]
+	140363291208064 -> 140363291208192
+	140363291208256 [label="Class: 7"]
+	140363291208064 -> 140363291208256
+	140363291208320 [label="Class: 6"]
+	140363291207424 -> 140363291208320
+	140363291208384 [label="Is feature citric acid(f_id: 2) <= 0.39?
+citric acid
+Treshold: 0.39"]
+	140363291207360 -> 140363291208384
+	140363291208512 [label="Is feature residual sugar(f_id: 3) <= 1.0?
+residual sugar
+Treshold: 1.0"]
+	140363291208384 -> 140363291208512
+	140363291208640 [label="Class: 6"]
+	140363291208512 -> 140363291208640
+	140363291208704 [label="Class: 7"]
+	140363291208512 -> 140363291208704
+	140363291208768 [label="Class: 5"]
+	140363291208384 -> 140363291208768
+	140363291208832 [label="Is feature free sulfur dioxide(f_id: 5) <= 38.0?
+free sulfur dioxide
+Treshold: 38.0"]
+	140363291207104 -> 140363291208832
+	140363291208896 [label="Is feature free sulfur dioxide(f_id: 5) <= 29.0?
+free sulfur dioxide
+Treshold: 29.0"]
+	140363291208832 -> 140363291208896
+	140363291208960 [label="Is feature residual sugar(f_id: 3) <= 0.9?
+residual sugar
+Treshold: 0.9"]
+	140363291208896 -> 140363291208960
+	140363291209088 [label="Class: 7"]
+	140363291208960 -> 140363291209088
+	140363291209152 [label="Is feature fixed acidity(f_id: 0) <= 7.1?
+fixed acidity
+Treshold: 7.1"]
+	140363291208960 -> 140363291209152
+	140363291209280 [label="Is feature chlorides(f_id: 4) <= 0.042?
+chlorides
+Treshold: 0.042"]
+	140363291209152 -> 140363291209280
+	140363291209408 [label="Is feature free sulfur dioxide(f_id: 5) <= 25.0?
+free sulfur dioxide
+Treshold: 25.0"]
+	140363291209280 -> 140363291209408
+	140363291209472 [label="Class: 6"]
+	140363291209408 -> 140363291209472
+	140363291209536 [label="Class: 5"]
+	140363291209408 -> 140363291209536
+	140363291209600 [label="Class: 6"]
+	140363291209280 -> 140363291209600
+	140363291209664 [label="Is feature total sulfur dioxide(f_id: 6) <= 111.0?
+total sulfur dioxide
+Treshold: 111.0"]
+	140363291209152 -> 140363291209664
+	140363291209728 [label="Class: 6"]
+	140363291209664 -> 140363291209728
+	140363291209792 [label="Class: 5"]
+	140363291209664 -> 140363291209792
+	140363291209856 [label="Is feature sulphates(f_id: 9) <= 0.61?
+sulphates
+Treshold: 0.61"]
+	140363291208896 -> 140363291209856
+	140363291209984 [label="Class: 6"]
+	140363291209856 -> 140363291209984
+	140363291210048 [label="Is feature free sulfur dioxide(f_id: 5) <= 31.0?
+free sulfur dioxide
+Treshold: 31.0"]
+	140363291209856 -> 140363291210048
+	140363291210112 [label="Is feature free sulfur dioxide(f_id: 5) <= 30.0?
+free sulfur dioxide
+Treshold: 30.0"]
+	140363291210048 -> 140363291210112
+	140363291210176 [label="Class: 8"]
+	140363291210112 -> 140363291210176
+	140363291210240 [label="Class: 7"]
+	140363291210112 -> 140363291210240
+	140363291210304 [label="Class: 6"]
+	140363291210048 -> 140363291210304
+	140363291210368 [label="Is feature pH(f_id: 8) <= 3.42?
+pH
+Treshold: 3.42"]
+	140363291208832 -> 140363291210368
+	140363291210496 [label="Is feature fixed acidity(f_id: 0) <= 7.3?
+fixed acidity
+Treshold: 7.3"]
+	140363291210368 -> 140363291210496
+	140363291210624 [label="Is feature pH(f_id: 8) <= 3.21?
+pH
+Treshold: 3.21"]
+	140363291210496 -> 140363291210624
+	140363291210752 [label="Is feature free sulfur dioxide(f_id: 5) <= 40.0?
+free sulfur dioxide
+Treshold: 40.0"]
+	140363291210624 -> 140363291210752
+	140363291210816 [label="Is feature citric acid(f_id: 2) <= 0.19?
+citric acid
+Treshold: 0.19"]
+	140363291210752 -> 140363291210816
+	140363291210944 [label="Class: 6"]
+	140363291210816 -> 140363291210944
+	140363291211008 [label="Class: 5"]
+	140363291210816 -> 140363291211008
+	140363291211072 [label="Class: 6"]
+	140363291210752 -> 140363291211072
+	140363291211136 [label="Is feature fixed acidity(f_id: 0) <= 6.7?
+fixed acidity
+Treshold: 6.7"]
+	140363291210624 -> 140363291211136
+	140363291211264 [label="Is feature pH(f_id: 8) <= 3.25?
+pH
+Treshold: 3.25"]
+	140363291211136 -> 140363291211264
+	140363291211392 [label="Is feature density(f_id: 7) <= 0.992?
+density
+Treshold: 0.992"]
+	140363291211264 -> 140363291211392
+	140363291211520 [label="Class: 6"]
+	140363291211392 -> 140363291211520
+	140363291211584 [label="Class: 7"]
+	140363291211392 -> 140363291211584
+	140363291211648 [label="Class: 6"]
+	140363291211264 -> 140363291211648
+	140363291211712 [label="Class: 7"]
+	140363291211136 -> 140363291211712
+	140363291211776 [label="Is feature chlorides(f_id: 4) <= 0.044?
+chlorides
+Treshold: 0.044"]
+	140363291210496 -> 140363291211776
+	140363291211904 [label="Class: 6"]
+	140363291211776 -> 140363291211904
+	140363291211968 [label="Is feature free sulfur dioxide(f_id: 5) <= 41.0?
+free sulfur dioxide
+Treshold: 41.0"]
+	140363291211776 -> 140363291211968
+	140363291212032 [label="Is feature residual sugar(f_id: 3) <= 4.8?
+residual sugar
+Treshold: 4.8"]
+	140363291211968 -> 140363291212032
+	140363291212160 [label="Class: 7"]
+	140363291212032 -> 140363291212160
+	140363291212224 [label="Class: 5"]
+	140363291212032 -> 140363291212224
+	140363291212288 [label="Class: 7"]
+	140363291211968 -> 140363291212288
+	140363291212352 [label="Is feature citric acid(f_id: 2) <= 0.42?
+citric acid
+Treshold: 0.42"]
+	140363291210368 -> 140363291212352
+	140363291212480 [label="Is feature pH(f_id: 8) <= 3.56?
+pH
+Treshold: 3.56"]
+	140363291212352 -> 140363291212480
+	140363291212608 [label="Is feature total sulfur dioxide(f_id: 6) <= 122.0?
+total sulfur dioxide
+Treshold: 122.0"]
+	140363291212480 -> 140363291212608
+	140363291212672 [label="Class: 8"]
+	140363291212608 -> 140363291212672
+	140363291212736 [label="Class: 5"]
+	140363291212608 -> 140363291212736
+	140363291212800 [label="Class: 7"]
+	140363291212480 -> 140363291212800
+	140363291212864 [label="Class: 6"]
+	140363291212352 -> 140363291212864
+	140363291212928 [label="Is feature density(f_id: 7) <= 0.99454?
+density
+Treshold: 0.99454"]
+	140363291207040 -> 140363291212928
+	140363291213056 [label="Is feature volatile acidity(f_id: 1) <= 0.24?
+volatile acidity
+Treshold: 0.24"]
+	140363291212928 -> 140363291213056
+	140363291213120 [label="Is feature total sulfur dioxide(f_id: 6) <= 190.0?
+total sulfur dioxide
+Treshold: 190.0"]
+	140363291213056 -> 140363291213120
+	140363291213184 [label="Class: 6"]
+	140363291213120 -> 140363291213184
+	140363291213248 [label="Class: 7"]
+	140363291213120 -> 140363291213248
+	140363291213312 [label="Class: 5"]
+	140363291213056 -> 140363291213312
+	140363291213376 [label="Is feature total sulfur dioxide(f_id: 6) <= 179.0?
+total sulfur dioxide
+Treshold: 179.0"]
+	140363291212928 -> 140363291213376
+	140363291213440 [label="Is feature alcohol(f_id: 10) <= 10.0?
+alcohol
+Treshold: 10.0"]
+	140363291213376 -> 140363291213440
+	140363291213568 [label="Is feature fixed acidity(f_id: 0) <= 6.7?
+fixed acidity
+Treshold: 6.7"]
+	140363291213440 -> 140363291213568
+	140363291213696 [label="Is feature chlorides(f_id: 4) <= 0.053?
+chlorides
+Treshold: 0.053"]
+	140363291213568 -> 140363291213696
+	140363291279424 [label="Class: 6"]
+	140363291213696 -> 140363291279424
+	140363291279488 [label="Class: 5"]
+	140363291213696 -> 140363291279488
+	140363291279552 [label="Class: 5"]
+	140363291213568 -> 140363291279552
+	140363291279616 [label="Class: 6"]
+	140363291213440 -> 140363291279616
+	140363291279680 [label="Is feature citric acid(f_id: 2) <= 0.49?
+citric acid
+Treshold: 0.49"]
+	140363291213376 -> 140363291279680
+	140363291279808 [label="Is feature pH(f_id: 8) <= 3.65?
+pH
+Treshold: 3.65"]
+	140363291279680 -> 140363291279808
+	140363291279936 [label="Class: 6"]
+	140363291279808 -> 140363291279936
+	140363291280000 [label="Class: 4"]
+	140363291279808 -> 140363291280000
+	140363291280064 [label="Class: 4"]
+	140363291279680 -> 140363291280064
+	140363291280128 [label="Is feature alcohol(f_id: 10) <= 10.0?
+alcohol
+Treshold: 10.0"]
+	140363292071872 -> 140363291280128
+	140363291280256 [label="Is feature free sulfur dioxide(f_id: 5) <= 17.0?
+free sulfur dioxide
+Treshold: 17.0"]
+	140363291280128 -> 140363291280256
+	140363291280320 [label="Is feature free sulfur dioxide(f_id: 5) <= 11.5?
+free sulfur dioxide
+Treshold: 11.5"]
+	140363291280256 -> 140363291280320
+	140363291280384 [label="Is feature alcohol(f_id: 10) <= 9.4?
+alcohol
+Treshold: 9.4"]
+	140363291280320 -> 140363291280384
+	140363291280512 [label="Is feature volatile acidity(f_id: 1) <= 0.33?
+volatile acidity
+Treshold: 0.33"]
+	140363291280384 -> 140363291280512
+	140363291280576 [label="Is feature fixed acidity(f_id: 0) <= 7.1?
+fixed acidity
+Treshold: 7.1"]
+	140363291280512 -> 140363291280576
+	140363291280704 [label="Class: 5"]
+	140363291280576 -> 140363291280704
+	140363291280768 [label="Is feature total sulfur dioxide(f_id: 6) <= 134.0?
+total sulfur dioxide
+Treshold: 134.0"]
+	140363291280576 -> 140363291280768
+	140363291280832 [label="Class: 3"]
+	140363291280768 -> 140363291280832
+	140363291280896 [label="Class: 5"]
+	140363291280768 -> 140363291280896
+	140363291280960 [label="Is feature pH(f_id: 8) <= 3.15?
+pH
+Treshold: 3.15"]
+	140363291280512 -> 140363291280960
+	140363291281088 [label="Is feature total sulfur dioxide(f_id: 6) <= 108.0?
+total sulfur dioxide
+Treshold: 108.0"]
+	140363291280960 -> 140363291281088
+	140363291281152 [label="Class: 4"]
+	140363291281088 -> 140363291281152
+	140363291281216 [label="Class: 5"]
+	140363291281088 -> 140363291281216
+	140363291281280 [label="Is feature alcohol(f_id: 10) <= 8.5?
+alcohol
+Treshold: 8.5"]
+	140363291280960 -> 140363291281280
+	140363291281408 [label="Class: 5"]
+	140363291281280 -> 140363291281408
+	140363291281472 [label="Class: 4"]
+	140363291281280 -> 140363291281472
+	140363291281536 [label="Is feature chlorides(f_id: 4) <= 0.049?
+chlorides
+Treshold: 0.049"]
+	140363291280384 -> 140363291281536
+	140363291281664 [label="Is feature volatile acidity(f_id: 1) <= 0.47?
+volatile acidity
+Treshold: 0.47"]
+	140363291281536 -> 140363291281664
+	140363291281728 [label="Is feature chlorides(f_id: 4) <= 0.034?
+chlorides
+Treshold: 0.034"]
+	140363291281664 -> 140363291281728
+	140363291281856 [label="Is feature total sulfur dioxide(f_id: 6) <= 110.0?
+total sulfur dioxide
+Treshold: 110.0"]
+	140363291281728 -> 140363291281856
+	140363291281920 [label="Class: 5"]
+	140363291281856 -> 140363291281920
+	140363291281984 [label="Class: 4"]
+	140363291281856 -> 140363291281984
+	140363291282048 [label="Is feature sulphates(f_id: 9) <= 0.46?
+sulphates
+Treshold: 0.46"]
+	140363291281728 -> 140363291282048
+	140363291282176 [label="Class: 6"]
+	140363291282048 -> 140363291282176
+	140363291282240 [label="Is feature alcohol(f_id: 10) <= 9.7?
+alcohol
+Treshold: 9.7"]
+	140363291282048 -> 140363291282240
+	140363291282368 [label="Class: 5"]
+	140363291282240 -> 140363291282368
+	140363291282432 [label="Is feature pH(f_id: 8) <= 3.19?
+pH
+Treshold: 3.19"]
+	140363291282240 -> 140363291282432
+	140363291282560 [label="Class: 5"]
+	140363291282432 -> 140363291282560
+	140363291282624 [label="Class: 6"]
+	140363291282432 -> 140363291282624
+	140363291282688 [label="Class: 4"]
+	140363291281664 -> 140363291282688
+	140363291282752 [label="Class: 4"]
+	140363291281536 -> 140363291282752
+	140363291282816 [label="Is feature alcohol(f_id: 10) <= 9.0?
+alcohol
+Treshold: 9.0"]
+	140363291280320 -> 140363291282816
+	140363291282944 [label="Is feature citric acid(f_id: 2) <= 0.0?
+citric acid
+Treshold: 0.0"]
+	140363291282816 -> 140363291282944
+	140363291283072 [label="Class: 5"]
+	140363291282944 -> 140363291283072
+	140363291283136 [label="Is feature alcohol(f_id: 10) <= 8.7?
+alcohol
+Treshold: 8.7"]
+	140363291282944 -> 140363291283136
+	140363291283264 [label="Class: 6"]
+	140363291283136 -> 140363291283264
+	140363291283328 [label="Class: 4"]
+	140363291283136 -> 140363291283328
+	140363291283392 [label="Is feature citric acid(f_id: 2) <= 0.19?
+citric acid
+Treshold: 0.19"]
+	140363291282816 -> 140363291283392
+	140363291283520 [label="Is feature chlorides(f_id: 4) <= 0.048?
+chlorides
+Treshold: 0.048"]
+	140363291283392 -> 140363291283520
+	140363291283648 [label="Class: 5"]
+	140363291283520 -> 140363291283648
+	140363291283712 [label="Class: 4"]
+	140363291283520 -> 140363291283712
+	140363291283776 [label="Is feature pH(f_id: 8) <= 3.28?
+pH
+Treshold: 3.28"]
+	140363291283392 -> 140363291283776
+	140363291283904 [label="Is feature citric acid(f_id: 2) <= 0.21?
+citric acid
+Treshold: 0.21"]
+	140363291283776 -> 140363291283904
+	140363291284032 [label="Is feature residual sugar(f_id: 3) <= 2.2?
+residual sugar
+Treshold: 2.2"]
+	140363291283904 -> 140363291284032
+	140363291284160 [label="Is feature fixed acidity(f_id: 0) <= 6.2?
+fixed acidity
+Treshold: 6.2"]
+	140363291284032 -> 140363291284160
+	140363291284288 [label="Class: 5"]
+	140363291284160 -> 140363291284288
+	140363291284352 [label="Class: 6"]
+	140363291284160 -> 140363291284352
+	140363291284416 [label="Class: 5"]
+	140363291284032 -> 140363291284416
+	140363291284480 [label="Class: 5"]
+	140363291283904 -> 140363291284480
+	140363291284544 [label="Is feature free sulfur dioxide(f_id: 5) <= 15.0?
+free sulfur dioxide
+Treshold: 15.0"]
+	140363291283776 -> 140363291284544
+	140363291284608 [label="Class: 6"]
+	140363291284544 -> 140363291284608
+	140363291284672 [label="Is feature sulphates(f_id: 9) <= 0.47?
+sulphates
+Treshold: 0.47"]
+	140363291284544 -> 140363291284672
+	140363291284800 [label="Class: 6"]
+	140363291284672 -> 140363291284800
+	140363291284864 [label="Class: 5"]
+	140363291284672 -> 140363291284864
+	140363291284928 [label="Is feature chlorides(f_id: 4) <= 0.048?
+chlorides
+Treshold: 0.048"]
+	140363291280256 -> 140363291284928
+	140363291285056 [label="Is feature pH(f_id: 8) <= 3.23?
+pH
+Treshold: 3.23"]
+	140363291284928 -> 140363291285056
+	140363291285184 [label="Is feature citric acid(f_id: 2) <= 0.22?
+citric acid
+Treshold: 0.22"]
+	140363291285056 -> 140363291285184
+	140363291285312 [label="Is feature residual sugar(f_id: 3) <= 0.9?
+residual sugar
+Treshold: 0.9"]
+	140363291285184 -> 140363291285312
+	140363291285440 [label="Is feature chlorides(f_id: 4) <= 0.032?
+chlorides
+Treshold: 0.032"]
+	140363291285312 -> 140363291285440
+	140363291285568 [label="Class: 5"]
+	140363291285440 -> 140363291285568
+	140363291285632 [label="Class: 4"]
+	140363291285440 -> 140363291285632
+	140363291285696 [label="Is feature free sulfur dioxide(f_id: 5) <= 87.0?
+free sulfur dioxide
+Treshold: 87.0"]
+	140363291285312 -> 140363291285696
+	140363291285760 [label="Is feature sulphates(f_id: 9) <= 0.47?
+sulphates
+Treshold: 0.47"]
+	140363291285696 -> 140363291285760
+	140363291285888 [label="Is feature volatile acidity(f_id: 1) <= 0.37?
+volatile acidity
+Treshold: 0.37"]
+	140363291285760 -> 140363291285888
+	140363291285952 [label="Is feature sulphates(f_id: 9) <= 0.41?
+sulphates
+Treshold: 0.41"]
+	140363291285888 -> 140363291285952
+	140363291286080 [label="Class: 5"]
+	140363291285952 -> 140363291286080
+	140363291286144 [label="Is feature alcohol(f_id: 10) <= 9.0?
+alcohol
+Treshold: 9.0"]
+	140363291285952 -> 140363291286144
+	140363291286272 [label="Class: 5"]
+	140363291286144 -> 140363291286272
+	140363291286336 [label="Is feature citric acid(f_id: 2) <= 0.09?
+citric acid
+Treshold: 0.09"]
+	140363291286144 -> 140363291286336
+	140363291286464 [label="Class: 5"]
+	140363291286336 -> 140363291286464
+	140363291286528 [label="Is feature citric acid(f_id: 2) <= 0.16?
+citric acid
+Treshold: 0.16"]
+	140363291286336 -> 140363291286528
+	140363291286656 [label="Class: 6"]
+	140363291286528 -> 140363291286656
+	140363291286720 [label="Is feature total sulfur dioxide(f_id: 6) <= 195.0?
+total sulfur dioxide
+Treshold: 195.0"]
+	140363291286528 -> 140363291286720
+	140363291286784 [label="Class: 5"]
+	140363291286720 -> 140363291286784
+	140363291286848 [label="Class: 6"]
+	140363291286720 -> 140363291286848
+	140363291286912 [label="Is feature sulphates(f_id: 9) <= 0.38?
+sulphates
+Treshold: 0.38"]
+	140363291285888 -> 140363291286912
+	140363291287040 [label="Class: 6"]
+	140363291286912 -> 140363291287040
+	140363291287104 [label="Class: 5"]
+	140363291286912 -> 140363291287104
+	140363291287168 [label="Class: 5"]
+	140363291285760 -> 140363291287168
+	140363291287232 [label="Class: 4"]
+	140363291285696 -> 140363291287232
+	140363291287296 [label="Is feature chlorides(f_id: 4) <= 0.036?
+chlorides
+Treshold: 0.036"]
+	140363291285184 -> 140363291287296
+	140363291287424 [label="Is feature sulphates(f_id: 9) <= 0.59?
+sulphates
+Treshold: 0.59"]
+	140363291287296 -> 140363291287424
+	140363291287552 [label="Is feature volatile acidity(f_id: 1) <= 0.32?
+volatile acidity
+Treshold: 0.32"]
+	140363291287424 -> 140363291287552
+	140363291287616 [label="Class: 6"]
+	140363291287552 -> 140363291287616
+	140363291287680 [label="Is feature chlorides(f_id: 4) <= 0.034?
+chlorides
+Treshold: 0.034"]
+	140363291287552 -> 140363291287680
+	140363291287808 [label="Class: 6"]
+	140363291287680 -> 140363291287808
+	140363291287872 [label="Class: 5"]
+	140363291287680 -> 140363291287872
+	140363291287936 [label="Is feature fixed acidity(f_id: 0) <= 6.2?
+fixed acidity
+Treshold: 6.2"]
+	140363291287424 -> 140363291287936
+	140363291288064 [label="Class: 5"]
+	140363291287936 -> 140363291288064
+	140363291288128 [label="Class: 7"]
+	140363291287936 -> 140363291288128
+	140363291288192 [label="Is feature citric acid(f_id: 2) <= 0.58?
+citric acid
+Treshold: 0.58"]
+	140363291287296 -> 140363291288192
+	140363291288320 [label="Is feature citric acid(f_id: 2) <= 0.3?
+citric acid
+Treshold: 0.3"]
+	140363291288192 -> 140363291288320
+	140363291288448 [label="Is feature free sulfur dioxide(f_id: 5) <= 41.0?
+free sulfur dioxide
+Treshold: 41.0"]
+	140363291288320 -> 140363291288448
+	140363291288512 [label="Is feature residual sugar(f_id: 3) <= 6.3?
+residual sugar
+Treshold: 6.3"]
+	140363291288448 -> 140363291288512
+	140363291288640 [label="Is feature citric acid(f_id: 2) <= 0.27?
+citric acid
+Treshold: 0.27"]
+	140363291288512 -> 140363291288640
+	140363291288768 [label="Class: 6"]
+	140363291288640 -> 140363291288768
+	140363291288832 [label="Class: 5"]
+	140363291288640 -> 140363291288832
+	140363291288896 [label="Is feature citric acid(f_id: 2) <= 0.23?
+citric acid
+Treshold: 0.23"]
+	140363291288512 -> 140363291288896
+	140363291289024 [label="Class: 6"]
+	140363291288896 -> 140363291289024
+	140363291289088 [label="Class: 5"]
+	140363291288896 -> 140363291289088
+	140363291289152 [label="Is feature pH(f_id: 8) <= 3.04?
+pH
+Treshold: 3.04"]
+	140363291288448 -> 140363291289152
+	140363291289280 [label="Is feature density(f_id: 7) <= 0.99782?
+density
+Treshold: 0.99782"]
+	140363291289152 -> 140363291289280
+	140363291289408 [label="Class: 7"]
+	140363291289280 -> 140363291289408
+	140363291289472 [label="Class: 6"]
+	140363291289280 -> 140363291289472
+	140363291289536 [label="Is feature density(f_id: 7) <= 0.9972?
+density
+Treshold: 0.9972"]
+	140363291289152 -> 140363291289536
+	140363291289664 [label="Is feature pH(f_id: 8) <= 3.09?
+pH
+Treshold: 3.09"]
+	140363291289536 -> 140363291289664
+	140363291289792 [label="Class: 4"]
+	140363291289664 -> 140363291289792
+	140363291289856 [label="Class: 6"]
+	140363291289664 -> 140363291289856
+	140363291289920 [label="Is feature volatile acidity(f_id: 1) <= 0.3?
+volatile acidity
+Treshold: 0.3"]
+	140363291289536 -> 140363291289920
+	140363291289984 [label="Is feature chlorides(f_id: 4) <= 0.042?
+chlorides
+Treshold: 0.042"]
+	140363291289920 -> 140363291289984
+	140363291290112 [label="Class: 5"]
+	140363291289984 -> 140363291290112
+	140363291290176 [label="Class: 6"]
+	140363291289984 -> 140363291290176
+	140363291290240 [label="Class: 5"]
+	140363291289920 -> 140363291290240
+	140363291290304 [label="Is feature residual sugar(f_id: 3) <= 6.2?
+residual sugar
+Treshold: 6.2"]
+	140363291288320 -> 140363291290304
+	140363291290432 [label="Is feature free sulfur dioxide(f_id: 5) <= 34.0?
+free sulfur dioxide
+Treshold: 34.0"]
+	140363291290304 -> 140363291290432
+	140363291290496 [label="Is feature density(f_id: 7) <= 0.9936?
+density
+Treshold: 0.9936"]
+	140363291290432 -> 140363291290496
+	140363291290624 [label="Is feature fixed acidity(f_id: 0) <= 5.7?
+fixed acidity
+Treshold: 5.7"]
+	140363291290496 -> 140363291290624
+	140363291290752 [label="Class: 5"]
+	140363291290624 -> 140363291290752
+	140363291290816 [label="Class: 6"]
+	140363291290624 -> 140363291290816
+	140363291290880 [label="Class: 5"]
+	140363291290496 -> 140363291290880
+	140363291290944 [label="Class: 6"]
+	140363291290432 -> 140363291290944
+	140363291291008 [label="Is feature fixed acidity(f_id: 0) <= 8.2?
+fixed acidity
+Treshold: 8.2"]
+	140363291290304 -> 140363291291008
+	140363291291136 [label="Is feature chlorides(f_id: 4) <= 0.045?
+chlorides
+Treshold: 0.045"]
+	140363291291008 -> 140363291291136
+	140363291291264 [label="Is feature fixed acidity(f_id: 0) <= 7.6?
+fixed acidity
+Treshold: 7.6"]
+	140363291291136 -> 140363291291264
+	140363291291392 [label="Is feature chlorides(f_id: 4) <= 0.044?
+chlorides
+Treshold: 0.044"]
+	140363291291264 -> 140363291291392
+	140363291291520 [label="Is feature alcohol(f_id: 10) <= 8.9?
+alcohol
+Treshold: 8.9"]
+	140363291291392 -> 140363291291520
+	140363291291648 [label="Class: 5"]
+	140363291291520 -> 140363291291648
+	140363291291712 [label="Is feature residual sugar(f_id: 3) <= 7.7?
+residual sugar
+Treshold: 7.7"]
+	140363291291520 -> 140363291291712
+	140363291291840 [label="Is feature alcohol(f_id: 10) <= 9.7?
+alcohol
+Treshold: 9.7"]
+	140363291291712 -> 140363291291840
+	140363291291968 [label="Class: 6"]
+	140363291291840 -> 140363291291968
+	140363291292032 [label="Class: 5"]
+	140363291291840 -> 140363291292032
+	140363291292096 [label="Is feature chlorides(f_id: 4) <= 0.042?
+chlorides
+Treshold: 0.042"]
+	140363291291712 -> 140363291292096
+	140363291292224 [label="Is feature chlorides(f_id: 4) <= 0.039?
+chlorides
+Treshold: 0.039"]
+	140363291292096 -> 140363291292224
+	140363291292352 [label="Class: 5"]
+	140363291292224 -> 140363291292352
+	140363291292416 [label="Is feature citric acid(f_id: 2) <= 0.33?
+citric acid
+Treshold: 0.33"]
+	140363291292224 -> 140363291292416
+	140363291292544 [label="Class: 5"]
+	140363291292416 -> 140363291292544
+	140363291292608 [label="Is feature free sulfur dioxide(f_id: 5) <= 61.0?
+free sulfur dioxide
+Treshold: 61.0"]
+	140363291292416 -> 140363291292608
+	140363291292672 [label="Is feature volatile acidity(f_id: 1) <= 0.28?
+volatile acidity
+Treshold: 0.28"]
+	140363291292608 -> 140363291292672
+	140363291292736 [label="Is feature fixed acidity(f_id: 0) <= 6.5?
+fixed acidity
+Treshold: 6.5"]
+	140363291292672 -> 140363291292736
+	140363291292864 [label="Class: 6"]
+	140363291292736 -> 140363291292864
+	140363291292928 [label="Class: 5"]
+	140363291292736 -> 140363291292928
+	140363291292992 [label="Class: 6"]
+	140363291292672 -> 140363291292992
+	140363291293056 [label="Class: 5"]
+	140363291292608 -> 140363291293056
+	140363291293120 [label="Class: 5"]
+	140363291292096 -> 140363291293120
+	140363291293184 [label="Is feature pH(f_id: 8) <= 3.16?
+pH
+Treshold: 3.16"]
+	140363291291392 -> 140363291293184
+	140363291293312 [label="Is feature residual sugar(f_id: 3) <= 6.8?
+residual sugar
+Treshold: 6.8"]
+	140363291293184 -> 140363291293312
+	140363291293440 [label="Class: 5"]
+	140363291293312 -> 140363291293440
+	140363291293504 [label="Class: 6"]
+	140363291293312 -> 140363291293504
+	140363291293568 [label="Class: 5"]
+	140363291293184 -> 140363291293568
+	140363291293632 [label="Class: 5"]
+	140363291291264 -> 140363291293632
+	140363291293696 [label="Is feature volatile acidity(f_id: 1) <= 0.33?
+volatile acidity
+Treshold: 0.33"]
+	140363291291136 -> 140363291293696
+	140363291293760 [label="Class: 5"]
+	140363291293696 -> 140363291293760
+	140363291293824 [label="Is feature citric acid(f_id: 2) <= 0.45?
+citric acid
+Treshold: 0.45"]
+	140363291293696 -> 140363291293824
+	140363291293952 [label="Class: 6"]
+	140363291293824 -> 140363291293952
+	140363291294016 [label="Class: 5"]
+	140363291293824 -> 140363291294016
+	140363291294080 [label="Class: 6"]
+	140363291291008 -> 140363291294080
+	140363291294144 [label="Is feature total sulfur dioxide(f_id: 6) <= 178.0?
+total sulfur dioxide
+Treshold: 178.0"]
+	140363291288192 -> 140363291294144
+	140363291294208 [label="Is feature citric acid(f_id: 2) <= 0.7?
+citric acid
+Treshold: 0.7"]
+	140363291294144 -> 140363291294208
+	140363291294336 [label="Is feature chlorides(f_id: 4) <= 0.044?
+chlorides
+Treshold: 0.044"]
+	140363291294208 -> 140363291294336
+	140363291294464 [label="Is feature density(f_id: 7) <= 0.9975?
+density
+Treshold: 0.9975"]
+	140363291294336 -> 140363291294464
+	140363291294592 [label="Class: 6"]
+	140363291294464 -> 140363291294592
+	140363291294656 [label="Is feature citric acid(f_id: 2) <= 0.69?
+citric acid
+Treshold: 0.69"]
+	140363291294464 -> 140363291294656
+	140363291294784 [label="Class: 5"]
+	140363291294656 -> 140363291294784
+	140363291294848 [label="Class: 6"]
+	140363291294656 -> 140363291294848
+	140363291294912 [label="Class: 5"]
+	140363291294336 -> 140363291294912
+	140363291294976 [label="Class: 5"]
+	140363291294208 -> 140363291294976
+	140363291295040 [label="Is feature residual sugar(f_id: 3) <= 11.1?
+residual sugar
+Treshold: 11.1"]
+	140363291294144 -> 140363291295040
+	140363291295168 [label="Class: 4"]
+	140363291295040 -> 140363291295168
+	140363291295232 [label="Is feature volatile acidity(f_id: 1) <= 0.59?
+volatile acidity
+Treshold: 0.59"]
+	140363291295040 -> 140363291295232
+	140363291295296 [label="Class: 5"]
+	140363291295232 -> 140363291295296
+	140363291295360 [label="Class: 4"]
+	140363291295232 -> 140363291295360
+	140363291295424 [label="Is feature total sulfur dioxide(f_id: 6) <= 88.0?
+total sulfur dioxide
+Treshold: 88.0"]
+	140363291285056 -> 140363291295424
+	140363291295488 [label="Is feature density(f_id: 7) <= 0.99265?
+density
+Treshold: 0.99265"]
+	140363291295424 -> 140363291295488
+	140363291295616 [label="Class: 7"]
+	140363291295488 -> 140363291295616
+	140363291295680 [label="Is feature citric acid(f_id: 2) <= 0.0?
+citric acid
+Treshold: 0.0"]
+	140363291295488 -> 140363291295680
+	140363291345024 [label="Class: 4"]
+	140363291295680 -> 140363291345024
+	140363291345088 [label="Class: 5"]
+	140363291295680 -> 140363291345088
+	140363291345152 [label="Is feature free sulfur dioxide(f_id: 5) <= 32.0?
+free sulfur dioxide
+Treshold: 32.0"]
+	140363291295424 -> 140363291345152
+	140363291345216 [label="Is feature total sulfur dioxide(f_id: 6) <= 142.0?
+total sulfur dioxide
+Treshold: 142.0"]
+	140363291345152 -> 140363291345216
+	140363291345280 [label="Is feature pH(f_id: 8) <= 3.44?
+pH
+Treshold: 3.44"]
+	140363291345216 -> 140363291345280
+	140363291345408 [label="Is feature free sulfur dioxide(f_id: 5) <= 20.0?
+free sulfur dioxide
+Treshold: 20.0"]
+	140363291345280 -> 140363291345408
+	140363291345472 [label="Class: 5"]
+	140363291345408 -> 140363291345472
+	140363291345536 [label="Is feature total sulfur dioxide(f_id: 6) <= 121.0?
+total sulfur dioxide
+Treshold: 121.0"]
+	140363291345408 -> 140363291345536
+	140363291345600 [label="Class: 6"]
+	140363291345536 -> 140363291345600
+	140363291345664 [label="Is feature density(f_id: 7) <= 0.99612?
+density
+Treshold: 0.99612"]
+	140363291345536 -> 140363291345664
+	140363291345792 [label="Class: 6"]
+	140363291345664 -> 140363291345792
+	140363291345856 [label="Class: 5"]
+	140363291345664 -> 140363291345856
+	140363291345920 [label="Is feature sulphates(f_id: 9) <= 0.45?
+sulphates
+Treshold: 0.45"]
+	140363291345280 -> 140363291345920
+	140363291346048 [label="Class: 7"]
+	140363291345920 -> 140363291346048
+	140363291346112 [label="Class: 5"]
+	140363291345920 -> 140363291346112
+	140363291346176 [label="Is feature fixed acidity(f_id: 0) <= 6.4?
+fixed acidity
+Treshold: 6.4"]
+	140363291345216 -> 140363291346176
+	140363291346304 [label="Is feature fixed acidity(f_id: 0) <= 6.1?
+fixed acidity
+Treshold: 6.1"]
+	140363291346176 -> 140363291346304
+	140363291346432 [label="Class: 5"]
+	140363291346304 -> 140363291346432
+	140363291346496 [label="Class: 7"]
+	140363291346304 -> 140363291346496
+	140363291346560 [label="Is feature volatile acidity(f_id: 1) <= 0.35?
+volatile acidity
+Treshold: 0.35"]
+	140363291346176 -> 140363291346560
+	140363291346624 [label="Class: 6"]
+	140363291346560 -> 140363291346624
+	140363291346688 [label="Class: 5"]
+	140363291346560 -> 140363291346688
+	140363291346752 [label="Is feature citric acid(f_id: 2) <= 0.4?
+citric acid
+Treshold: 0.4"]
+	140363291345152 -> 140363291346752
+	140363291346880 [label="Is feature sulphates(f_id: 9) <= 0.36?
+sulphates
+Treshold: 0.36"]
+	140363291346752 -> 140363291346880
+	140363291347008 [label="Class: 5"]
+	140363291346880 -> 140363291347008
+	140363291347072 [label="Is feature sulphates(f_id: 9) <= 0.39?
+sulphates
+Treshold: 0.39"]
+	140363291346880 -> 140363291347072
+	140363291347200 [label="Class: 6"]
+	140363291347072 -> 140363291347200
+	140363291347264 [label="Is feature sulphates(f_id: 9) <= 0.48?
+sulphates
+Treshold: 0.48"]
+	140363291347072 -> 140363291347264
+	140363291347392 [label="Is feature volatile acidity(f_id: 1) <= 0.27?
+volatile acidity
+Treshold: 0.27"]
+	140363291347264 -> 140363291347392
+	140363291347456 [label="Is feature free sulfur dioxide(f_id: 5) <= 49.0?
+free sulfur dioxide
+Treshold: 49.0"]
+	140363291347392 -> 140363291347456
+	140363291347520 [label="Class: 5"]
+	140363291347456 -> 140363291347520
+	140363291347584 [label="Class: 6"]
+	140363291347456 -> 140363291347584
+	140363291347648 [label="Class: 5"]
+	140363291347392 -> 140363291347648
+	140363291347712 [label="Is feature pH(f_id: 8) <= 3.33?
+pH
+Treshold: 3.33"]
+	140363291347264 -> 140363291347712
+	140363291347840 [label="Is feature alcohol(f_id: 10) <= 9.4?
+alcohol
+Treshold: 9.4"]
+	140363291347712 -> 140363291347840
+	140363291347968 [label="Class: 5"]
+	140363291347840 -> 140363291347968
+	140363291348032 [label="Is feature alcohol(f_id: 10) <= 9.6?
+alcohol
+Treshold: 9.6"]
+	140363291347840 -> 140363291348032
+	140363291348160 [label="Class: 6"]
+	140363291348032 -> 140363291348160
+	140363291348224 [label="Is feature total sulfur dioxide(f_id: 6) <= 166.0?
+total sulfur dioxide
+Treshold: 166.0"]
+	140363291348032 -> 140363291348224
+	140363291348288 [label="Is feature total sulfur dioxide(f_id: 6) <= 147.0?
+total sulfur dioxide
+Treshold: 147.0"]
+	140363291348224 -> 140363291348288
+	140363291348352 [label="Class: 5"]
+	140363291348288 -> 140363291348352
+	140363291348416 [label="Class: 6"]
+	140363291348288 -> 140363291348416
+	140363291348480 [label="Class: 5"]
+	140363291348224 -> 140363291348480
+	140363291348544 [label="Is feature citric acid(f_id: 2) <= 0.22?
+citric acid
+Treshold: 0.22"]
+	140363291347712 -> 140363291348544
+	140363291348672 [label="Is feature total sulfur dioxide(f_id: 6) <= 147.0?
+total sulfur dioxide
+Treshold: 147.0"]
+	140363291348544 -> 140363291348672
+	140363291348736 [label="Class: 6"]
+	140363291348672 -> 140363291348736
+	140363291348800 [label="Is feature free sulfur dioxide(f_id: 5) <= 33.0?
+free sulfur dioxide
+Treshold: 33.0"]
+	140363291348672 -> 140363291348800
+	140363291348864 [label="Class: 6"]
+	140363291348800 -> 140363291348864
+	140363291348928 [label="Class: 5"]
+	140363291348800 -> 140363291348928
+	140363291348992 [label="Class: 6"]
+	140363291348544 -> 140363291348992
+	140363291349056 [label="Is feature density(f_id: 7) <= 0.99884?
+density
+Treshold: 0.99884"]
+	140363291346752 -> 140363291349056
+	140363291349184 [label="Class: 6"]
+	140363291349056 -> 140363291349184
+	140363291349248 [label="Class: 7"]
+	140363291349056 -> 140363291349248
+	140363291349312 [label="Is feature volatile acidity(f_id: 1) <= 0.56?
+volatile acidity
+Treshold: 0.56"]
+	140363291284928 -> 140363291349312
+	140363291349376 [label="Is feature chlorides(f_id: 4) <= 0.052?
+chlorides
+Treshold: 0.052"]
+	140363291349312 -> 140363291349376
+	140363291349504 [label="Is feature citric acid(f_id: 2) <= 0.5?
+citric acid
+Treshold: 0.5"]
+	140363291349376 -> 140363291349504
+	140363291349632 [label="Is feature alcohol(f_id: 10) <= 9.0?
+alcohol
+Treshold: 9.0"]
+	140363291349504 -> 140363291349632
+	140363291349760 [label="Is feature sulphates(f_id: 9) <= 0.52?
+sulphates
+Treshold: 0.52"]
+	140363291349632 -> 140363291349760
+	140363291349888 [label="Class: 5"]
+	140363291349760 -> 140363291349888
+	140363291349952 [label="Is feature chlorides(f_id: 4) <= 0.051?
+chlorides
+Treshold: 0.051"]
+	140363291349760 -> 140363291349952
+	140363291350080 [label="Class: 5"]
+	140363291349952 -> 140363291350080
+	140363291350144 [label="Class: 6"]
+	140363291349952 -> 140363291350144
+	140363291350208 [label="Is feature residual sugar(f_id: 3) <= 9.2?
+residual sugar
+Treshold: 9.2"]
+	140363291349632 -> 140363291350208
+	140363291350336 [label="Is feature sulphates(f_id: 9) <= 0.68?
+sulphates
+Treshold: 0.68"]
+	140363291350208 -> 140363291350336
+	140363291350464 [label="Is feature chlorides(f_id: 4) <= 0.05?
+chlorides
+Treshold: 0.05"]
+	140363291350336 -> 140363291350464
+	140363291350592 [label="Is feature free sulfur dioxide(f_id: 5) <= 57.0?
+free sulfur dioxide
+Treshold: 57.0"]
+	140363291350464 -> 140363291350592
+	140363291350656 [label="Is feature free sulfur dioxide(f_id: 5) <= 40.0?
+free sulfur dioxide
+Treshold: 40.0"]
+	140363291350592 -> 140363291350656
+	140363291350720 [label="Is feature total sulfur dioxide(f_id: 6) <= 155.0?
+total sulfur dioxide
+Treshold: 155.0"]
+	140363291350656 -> 140363291350720
+	140363291350784 [label="Is feature citric acid(f_id: 2) <= 0.44?
+citric acid
+Treshold: 0.44"]
+	140363291350720 -> 140363291350784
+	140363291350912 [label="Class: 5"]
+	140363291350784 -> 140363291350912
+	140363291350976 [label="Class: 6"]
+	140363291350784 -> 140363291350976
+	140363291351040 [label="Class: 6"]
+	140363291350720 -> 140363291351040
+	140363291351104 [label="Class: 5"]
+	140363291350656 -> 140363291351104
+	140363291351168 [label="Class: 6"]
+	140363291350592 -> 140363291351168
+	140363291351232 [label="Class: 5"]
+	140363291350464 -> 140363291351232
+	140363291351296 [label="Class: 6"]
+	140363291350336 -> 140363291351296
+	140363291351360 [label="Is feature density(f_id: 7) <= 0.9969?
+density
+Treshold: 0.9969"]
+	140363291350208 -> 140363291351360
+	140363291351488 [label="Class: 6"]
+	140363291351360 -> 140363291351488
+	140363291351552 [label="Is feature pH(f_id: 8) <= 3.24?
+pH
+Treshold: 3.24"]
+	140363291351360 -> 140363291351552
+	140363291351680 [label="Is feature residual sugar(f_id: 3) <= 18.35?
+residual sugar
+Treshold: 18.35"]
+	140363291351552 -> 140363291351680
+	140363291351808 [label="Is feature citric acid(f_id: 2) <= 0.32?
+citric acid
+Treshold: 0.32"]
+	140363291351680 -> 140363291351808
+	140363291351936 [label="Class: 5"]
+	140363291351808 -> 140363291351936
+	140363291352000 [label="Class: 6"]
+	140363291351808 -> 140363291352000
+	140363291352064 [label="Class: 6"]
+	140363291351680 -> 140363291352064
+	140363291352128 [label="Class: 6"]
+	140363291351552 -> 140363291352128
+	140363291352192 [label="Is feature volatile acidity(f_id: 1) <= 0.3?
+volatile acidity
+Treshold: 0.3"]
+	140363291349504 -> 140363291352192
+	140363291352256 [label="Is feature residual sugar(f_id: 3) <= 7.6?
+residual sugar
+Treshold: 7.6"]
+	140363291352192 -> 140363291352256
+	140363291352384 [label="Class: 4"]
+	140363291352256 -> 140363291352384
+	140363291352448 [label="Is feature volatile acidity(f_id: 1) <= 0.29?
+volatile acidity
+Treshold: 0.29"]
+	140363291352256 -> 140363291352448
+	140363291352512 [label="Class: 6"]
+	140363291352448 -> 140363291352512
+	140363291352576 [label="Class: 7"]
+	140363291352448 -> 140363291352576
+	140363291352640 [label="Is feature free sulfur dioxide(f_id: 5) <= 20.0?
+free sulfur dioxide
+Treshold: 20.0"]
+	140363291352192 -> 140363291352640
+	140363291352704 [label="Class: 4"]
+	140363291352640 -> 140363291352704
+	140363291352768 [label="Class: 5"]
+	140363291352640 -> 140363291352768
+	140363291352832 [label="Is feature fixed acidity(f_id: 0) <= 7.7?
+fixed acidity
+Treshold: 7.7"]
+	140363291349376 -> 140363291352832
+	140363291352960 [label="Is feature fixed acidity(f_id: 0) <= 7.4?
+fixed acidity
+Treshold: 7.4"]
+	140363291352832 -> 140363291352960
+	140363291353088 [label="Is feature density(f_id: 7) <= 0.994?
+density
+Treshold: 0.994"]
+	140363291352960 -> 140363291353088
+	140363291353216 [label="Is feature residual sugar(f_id: 3) <= 3.5?
+residual sugar
+Treshold: 3.5"]
+	140363291353088 -> 140363291353216
+	140363291353344 [label="Is feature density(f_id: 7) <= 0.9936?
+density
+Treshold: 0.9936"]
+	140363291353216 -> 140363291353344
+	140363291353472 [label="Is feature citric acid(f_id: 2) <= 0.15?
+citric acid
+Treshold: 0.15"]
+	140363291353344 -> 140363291353472
+	140363291353600 [label="Class: 5"]
+	140363291353472 -> 140363291353600
+	140363291353664 [label="Is feature sulphates(f_id: 9) <= 0.43?
+sulphates
+Treshold: 0.43"]
+	140363291353472 -> 140363291353664
+	140363291353792 [label="Class: 6"]
+	140363291353664 -> 140363291353792
+	140363291353856 [label="Is feature volatile acidity(f_id: 1) <= 0.335?
+volatile acidity
+Treshold: 0.335"]
+	140363291353664 -> 140363291353856
+	140363291353920 [label="Is feature alcohol(f_id: 10) <= 9.9?
+alcohol
+Treshold: 9.9"]
+	140363291353856 -> 140363291353920
+	140363291354048 [label="Class: 6"]
+	140363291353920 -> 140363291354048
+	140363291354112 [label="Class: 5"]
+	140363291353920 -> 140363291354112
+	140363291354176 [label="Is feature total sulfur dioxide(f_id: 6) <= 178.0?
+total sulfur dioxide
+Treshold: 178.0"]
+	140363291353856 -> 140363291354176
+	140363291354240 [label="Class: 5"]
+	140363291354176 -> 140363291354240
+	140363291354304 [label="Class: 6"]
+	140363291354176 -> 140363291354304
+	140363291354368 [label="Is feature fixed acidity(f_id: 0) <= 6.8?
+fixed acidity
+Treshold: 6.8"]
+	140363291353344 -> 140363291354368
+	140363291354496 [label="Class: 6"]
+	140363291354368 -> 140363291354496
+	140363291354560 [label="Class: 4"]
+	140363291354368 -> 140363291354560
+	140363291354624 [label="Class: 4"]
+	140363291353216 -> 140363291354624
+	140363291354688 [label="Is feature citric acid(f_id: 2) <= 0.2?
+citric acid
+Treshold: 0.2"]
+	140363291353088 -> 140363291354688
+	140363291354816 [label="Is feature free sulfur dioxide(f_id: 5) <= 50.0?
+free sulfur dioxide
+Treshold: 50.0"]
+	140363291354688 -> 140363291354816
+	140363291354880 [label="Is feature total sulfur dioxide(f_id: 6) <= 158.0?
+total sulfur dioxide
+Treshold: 158.0"]
+	140363291354816 -> 140363291354880
+	140363291354944 [label="Is feature free sulfur dioxide(f_id: 5) <= 24.0?
+free sulfur dioxide
+Treshold: 24.0"]
+	140363291354880 -> 140363291354944
+	140363291355008 [label="Class: 5"]
+	140363291354944 -> 140363291355008
+	140363291355072 [label="Class: 6"]
+	140363291354944 -> 140363291355072
+	140363291355136 [label="Is feature fixed acidity(f_id: 0) <= 6.4?
+fixed acidity
+Treshold: 6.4"]
+	140363291354880 -> 140363291355136
+	140363291355264 [label="Is feature free sulfur dioxide(f_id: 5) <= 36.0?
+free sulfur dioxide
+Treshold: 36.0"]
+	140363291355136 -> 140363291355264
+	140363291355328 [label="Is feature volatile acidity(f_id: 1) <= 0.27?
+volatile acidity
+Treshold: 0.27"]
+	140363291355264 -> 140363291355328
+	140363291355392 [label="Class: 4"]
+	140363291355328 -> 140363291355392
+	140363291355456 [label="Class: 5"]
+	140363291355328 -> 140363291355456
+	140363291355520 [label="Class: 4"]
+	140363291355264 -> 140363291355520
+	140363291355584 [label="Class: 5"]
+	140363291355136 -> 140363291355584
+	140363291355648 [label="Is feature volatile acidity(f_id: 1) <= 0.36?
+volatile acidity
+Treshold: 0.36"]
+	140363291354816 -> 140363291355648
+	140363291355712 [label="Class: 6"]
+	140363291355648 -> 140363291355712
+	140363291355776 [label="Is feature free sulfur dioxide(f_id: 5) <= 57.0?
+free sulfur dioxide
+Treshold: 57.0"]
+	140363291355648 -> 140363291355776
+	140363291355840 [label="Class: 6"]
+	140363291355776 -> 140363291355840
+	140363291355904 [label="Class: 5"]
+	140363291355776 -> 140363291355904
+	140363291355968 [label="Is feature free sulfur dioxide(f_id: 5) <= 53.0?
+free sulfur dioxide
+Treshold: 53.0"]
+	140363291354688 -> 140363291355968
+	140363291356032 [label="Is feature volatile acidity(f_id: 1) <= 0.42?
+volatile acidity
+Treshold: 0.42"]
+	140363291355968 -> 140363291356032
+	140363291356096 [label="Is feature citric acid(f_id: 2) <= 0.23?
+citric acid
+Treshold: 0.23"]
+	140363291356032 -> 140363291356096
+	140363291356224 [label="Class: 5"]
+	140363291356096 -> 140363291356224
+	140363291356288 [label="Is feature free sulfur dioxide(f_id: 5) <= 22.0?
+free sulfur dioxide
+Treshold: 22.0"]
+	140363291356096 -> 140363291356288
+	140363291356352 [label="Class: 6"]
+	140363291356288 -> 140363291356352
+	140363291356416 [label="Is feature sulphates(f_id: 9) <= 0.43?
+sulphates
+Treshold: 0.43"]
+	140363291356288 -> 140363291356416
+	140363291356544 [label="Is feature citric acid(f_id: 2) <= 0.28?
+citric acid
+Treshold: 0.28"]
+	140363291356416 -> 140363291356544
+	140363291356672 [label="Is feature chlorides(f_id: 4) <= 0.054?
+chlorides
+Treshold: 0.054"]
+	140363291356544 -> 140363291356672
+	140363291356800 [label="Class: 4"]
+	140363291356672 -> 140363291356800
+	140363291356864 [label="Class: 6"]
+	140363291356672 -> 140363291356864
+	140363291356928 [label="Is feature alcohol(f_id: 10) <= 9.0?
+alcohol
+Treshold: 9.0"]
+	140363291356544 -> 140363291356928
+	140363291357056 [label="Class: 6"]
+	140363291356928 -> 140363291357056
+	140363291357120 [label="Class: 5"]
+	140363291356928 -> 140363291357120
+	140363291357184 [label="Is feature density(f_id: 7) <= 0.9988?
+density
+Treshold: 0.9988"]
+	140363291356416 -> 140363291357184
+	140363291357312 [label="Is feature pH(f_id: 8) <= 3.09?
+pH
+Treshold: 3.09"]
+	140363291357184 -> 140363291357312
+	140363291357440 [label="Class: 5"]
+	140363291357312 -> 140363291357440
+	140363291357504 [label="Is feature citric acid(f_id: 2) <= 0.24?
+citric acid
+Treshold: 0.24"]
+	140363291357312 -> 140363291357504
+	140363291357632 [label="Class: 5"]
+	140363291357504 -> 140363291357632
+	140363291357696 [label="Is feature chlorides(f_id: 4) <= 0.063?
+chlorides
+Treshold: 0.063"]
+	140363291357504 -> 140363291357696
+	140363291357824 [label="Is feature pH(f_id: 8) <= 3.19?
+pH
+Treshold: 3.19"]
+	140363291357696 -> 140363291357824
+	140363291357952 [label="Is feature chlorides(f_id: 4) <= 0.058?
+chlorides
+Treshold: 0.058"]
+	140363291357824 -> 140363291357952
+	140363291358080 [label="Class: 5"]
+	140363291357952 -> 140363291358080
+	140363291358144 [label="Is feature pH(f_id: 8) <= 3.14?
+pH
+Treshold: 3.14"]
+	140363291357952 -> 140363291358144
+	140363291358272 [label="Class: 6"]
+	140363291358144 -> 140363291358272
+	140363291358336 [label="Class: 5"]
+	140363291358144 -> 140363291358336
+	140363291358400 [label="Is feature pH(f_id: 8) <= 3.26?
+pH
+Treshold: 3.26"]
+	140363291357824 -> 140363291358400
+	140363291358528 [label="Class: 6"]
+	140363291358400 -> 140363291358528
+	140363291358592 [label="Is feature free sulfur dioxide(f_id: 5) <= 42.0?
+free sulfur dioxide
+Treshold: 42.0"]
+	140363291358400 -> 140363291358592
+	140363291358656 [label="Class: 5"]
+	140363291358592 -> 140363291358656
+	140363291358720 [label="Class: 6"]
+	140363291358592 -> 140363291358720
+	140363291358784 [label="Is feature pH(f_id: 8) <= 3.19?
+pH
+Treshold: 3.19"]
+	140363291357696 -> 140363291358784
+	140363291358912 [label="Class: 6"]
+	140363291358784 -> 140363291358912
+	140363291358976 [label="Class: 5"]
+	140363291358784 -> 140363291358976
+	140363291359040 [label="Is feature volatile acidity(f_id: 1) <= 0.28?
+volatile acidity
+Treshold: 0.28"]
+	140363291357184 -> 140363291359040
+	140363291359104 [label="Is feature free sulfur dioxide(f_id: 5) <= 32.0?
+free sulfur dioxide
+Treshold: 32.0"]
+	140363291359040 -> 140363291359104
+	140363291359168 [label="Class: 6"]
+	140363291359104 -> 140363291359168
+	140363291359232 [label="Class: 5"]
+	140363291359104 -> 140363291359232
+	140363291359296 [label="Class: 6"]
+	140363291359040 -> 140363291359296
+	140363291359360 [label="Class: 5"]
+	140363291356032 -> 140363291359360
+	140363291359424 [label="Is feature alcohol(f_id: 10) <= 9.0?
+alcohol
+Treshold: 9.0"]
+	140363291355968 -> 140363291359424
+	140363291359552 [label="Is feature total sulfur dioxide(f_id: 6) <= 193.0?
+total sulfur dioxide
+Treshold: 193.0"]
+	140363291359424 -> 140363291359552
+	140363291359616 [label="Is feature citric acid(f_id: 2) <= 0.28?
+citric acid
+Treshold: 0.28"]
+	140363291359552 -> 140363291359616
+	140363291359744 [label="Class: 5"]
+	140363291359616 -> 140363291359744
+	140363291359808 [label="Class: 4"]
+	140363291359616 -> 140363291359808
+	140363291359872 [label="Class: 5"]
+	140363291359552 -> 140363291359872
+	140363291359936 [label="Is feature pH(f_id: 8) <= 3.11?
+pH
+Treshold: 3.11"]
+	140363291359424 -> 140363291359936
+	140363291360064 [label="Class: 5"]
+	140363291359936 -> 140363291360064
+	140363291360128 [label="Is feature citric acid(f_id: 2) <= 0.31?
+citric acid
+Treshold: 0.31"]
+	140363291359936 -> 140363291360128
+	140363291360256 [label="Class: 5"]
+	140363291360128 -> 140363291360256
+	140363291360320 [label="Is feature pH(f_id: 8) <= 3.23?
+pH
+Treshold: 3.23"]
+	140363291360128 -> 140363291360320
+	140363291360448 [label="Class: 6"]
+	140363291360320 -> 140363291360448
+	140363291360512 [label="Class: 5"]
+	140363291360320 -> 140363291360512
+	140363291360576 [label="Is feature density(f_id: 7) <= 0.99803?
+density
+Treshold: 0.99803"]
+	140363291352960 -> 140363291360576
+	140363291360704 [label="Is feature sulphates(f_id: 9) <= 0.62?
+sulphates
+Treshold: 0.62"]
+	140363291360576 -> 140363291360704
+	140363291360832 [label="Class: 5"]
+	140363291360704 -> 140363291360832
+	140363291360896 [label="Is feature sulphates(f_id: 9) <= 0.63?
+sulphates
+Treshold: 0.63"]
+	140363291360704 -> 140363291360896
+	140363291361024 [label="Class: 6"]
+	140363291360896 -> 140363291361024
+	140363291361088 [label="Class: 5"]
+	140363291360896 -> 140363291361088
+	140363291361152 [label="Is feature density(f_id: 7) <= 0.9989?
+density
+Treshold: 0.9989"]
+	140363291360576 -> 140363291361152
+	140363184488512 [label="Is feature total sulfur dioxide(f_id: 6) <= 191.0?
+total sulfur dioxide
+Treshold: 191.0"]
+	140363291361152 -> 140363184488512
+	140363184488576 [label="Class: 4"]
+	140363184488512 -> 140363184488576
+	140363184488640 [label="Class: 5"]
+	140363184488512 -> 140363184488640
+	140363184488704 [label="Class: 5"]
+	140363291361152 -> 140363184488704
+	140363184488768 [label="Is feature sulphates(f_id: 9) <= 0.46?
+sulphates
+Treshold: 0.46"]
+	140363291352832 -> 140363184488768
+	140363184488896 [label="Class: 6"]
+	140363184488768 -> 140363184488896
+	140363184488960 [label="Is feature total sulfur dioxide(f_id: 6) <= 190.0?
+total sulfur dioxide
+Treshold: 190.0"]
+	140363184488768 -> 140363184488960
+	140363184489024 [label="Is feature residual sugar(f_id: 3) <= 12.4?
+residual sugar
+Treshold: 12.4"]
+	140363184488960 -> 140363184489024
+	140363184489152 [label="Is feature alcohol(f_id: 10) <= 9.5?
+alcohol
+Treshold: 9.5"]
+	140363184489024 -> 140363184489152
+	140363184489280 [label="Class: 5"]
+	140363184489152 -> 140363184489280
+	140363184489344 [label="Class: 6"]
+	140363184489152 -> 140363184489344
+	140363184489408 [label="Class: 4"]
+	140363184489024 -> 140363184489408
+	140363184489472 [label="Class: 6"]
+	140363184488960 -> 140363184489472
+	140363184489536 [label="Is feature volatile acidity(f_id: 1) <= 0.64?
+volatile acidity
+Treshold: 0.64"]
+	140363291349312 -> 140363184489536
+	140363184489600 [label="Is feature citric acid(f_id: 2) <= 0.1?
+citric acid
+Treshold: 0.1"]
+	140363184489536 -> 140363184489600
+	140363184489728 [label="Class: 4"]
+	140363184489600 -> 140363184489728
+	140363184489792 [label="Is feature fixed acidity(f_id: 0) <= 7.4?
+fixed acidity
+Treshold: 7.4"]
+	140363184489600 -> 140363184489792
+	140363184489920 [label="Class: 5"]
+	140363184489792 -> 140363184489920
+	140363184489984 [label="Is feature chlorides(f_id: 4) <= 0.244?
+chlorides
+Treshold: 0.244"]
+	140363184489792 -> 140363184489984
+	140363184490112 [label="Class: 3"]
+	140363184489984 -> 140363184490112
+	140363184490176 [label="Class: 5"]
+	140363184489984 -> 140363184490176
+	140363184490240 [label="Is feature total sulfur dioxide(f_id: 6) <= 222.0?
+total sulfur dioxide
+Treshold: 222.0"]
+	140363184489536 -> 140363184490240
+	140363184490304 [label="Is feature pH(f_id: 8) <= 3.0?
+pH
+Treshold: 3.0"]
+	140363184490240 -> 140363184490304
+	140363184490432 [label="Class: 6"]
+	140363184490304 -> 140363184490432
+	140363184490496 [label="Is feature citric acid(f_id: 2) <= 0.42?
+citric acid
+Treshold: 0.42"]
+	140363184490304 -> 140363184490496
+	140363184490624 [label="Class: 4"]
+	140363184490496 -> 140363184490624
+	140363184490688 [label="Class: 6"]
+	140363184490496 -> 140363184490688
+	140363184490752 [label="Class: 5"]
+	140363184490240 -> 140363184490752
+	140363184490816 [label="Is feature free sulfur dioxide(f_id: 5) <= 20.0?
+free sulfur dioxide
+Treshold: 20.0"]
+	140363291280128 -> 140363184490816
+	140363184490880 [label="Is feature total sulfur dioxide(f_id: 6) <= 57.0?
+total sulfur dioxide
+Treshold: 57.0"]
+	140363184490816 -> 140363184490880
+	140363184490944 [label="Is feature volatile acidity(f_id: 1) <= 0.27?
+volatile acidity
+Treshold: 0.27"]
+	140363184490880 -> 140363184490944
+	140363184491008 [label="Class: 6"]
+	140363184490944 -> 140363184491008
+	140363184491072 [label="Is feature free sulfur dioxide(f_id: 5) <= 5.0?
+free sulfur dioxide
+Treshold: 5.0"]
+	140363184490944 -> 140363184491072
+	140363184491136 [label="Class: 3"]
+	140363184491072 -> 140363184491136
+	140363184491200 [label="Class: 4"]
+	140363184491072 -> 140363184491200
+	140363184491264 [label="Is feature chlorides(f_id: 4) <= 0.036?
+chlorides
+Treshold: 0.036"]
+	140363184490880 -> 140363184491264
+	140363184491392 [label="Is feature fixed acidity(f_id: 0) <= 6.1?
+fixed acidity
+Treshold: 6.1"]
+	140363184491264 -> 140363184491392
+	140363184491520 [label="Class: 6"]
+	140363184491392 -> 140363184491520
+	140363184491584 [label="Is feature residual sugar(f_id: 3) <= 10.7?
+residual sugar
+Treshold: 10.7"]
+	140363184491392 -> 140363184491584
+	140363184491712 [label="Is feature sulphates(f_id: 9) <= 0.61?
+sulphates
+Treshold: 0.61"]
+	140363184491584 -> 140363184491712
+	140363184491840 [label="Is feature residual sugar(f_id: 3) <= 8.7?
+residual sugar
+Treshold: 8.7"]
+	140363184491712 -> 140363184491840
+	140363184491968 [label="Class: 5"]
+	140363184491840 -> 140363184491968
+	140363184492032 [label="Is feature sulphates(f_id: 9) <= 0.36?
+sulphates
+Treshold: 0.36"]
+	140363184491840 -> 140363184492032
+	140363184492160 [label="Class: 6"]
+	140363184492032 -> 140363184492160
+	140363184492224 [label="Class: 5"]
+	140363184492032 -> 140363184492224
+	140363184492288 [label="Class: 6"]
+	140363184491712 -> 140363184492288
+	140363184492352 [label="Class: 4"]
+	140363184491584 -> 140363184492352
+	140363184492416 [label="Is feature total sulfur dioxide(f_id: 6) <= 84.0?
+total sulfur dioxide
+Treshold: 84.0"]
+	140363184491264 -> 140363184492416
+	140363184492480 [label="Is feature pH(f_id: 8) <= 3.22?
+pH
+Treshold: 3.22"]
+	140363184492416 -> 140363184492480
+	140363184492608 [label="Is feature total sulfur dioxide(f_id: 6) <= 59.0?
+total sulfur dioxide
+Treshold: 59.0"]
+	140363184492480 -> 140363184492608
+	140363184492672 [label="Class: 6"]
+	140363184492608 -> 140363184492672
+	140363184492736 [label="Class: 4"]
+	140363184492608 -> 140363184492736
+	140363184492800 [label="Is feature chlorides(f_id: 4) <= 0.045?
+chlorides
+Treshold: 0.045"]
+	140363184492480 -> 140363184492800
+	140363184492928 [label="Class: 5"]
+	140363184492800 -> 140363184492928
+	140363184492992 [label="Class: 4"]
+	140363184492800 -> 140363184492992
+	140363184493056 [label="Is feature density(f_id: 7) <= 0.9944?
+density
+Treshold: 0.9944"]
+	140363184492416 -> 140363184493056
+	140363184493184 [label="Is feature sulphates(f_id: 9) <= 0.39?
+sulphates
+Treshold: 0.39"]
+	140363184493056 -> 140363184493184
+	140363184493312 [label="Is feature sulphates(f_id: 9) <= 0.31?
+sulphates
+Treshold: 0.31"]
+	140363184493184 -> 140363184493312
+	140363184493440 [label="Class: 4"]
+	140363184493312 -> 140363184493440
+	140363184493504 [label="Class: 5"]
+	140363184493312 -> 140363184493504
+	140363184493568 [label="Is feature citric acid(f_id: 2) <= 0.3?
+citric acid
+Treshold: 0.3"]
+	140363184493184 -> 140363184493568
+	140363184493696 [label="Is feature pH(f_id: 8) <= 3.48?
+pH
+Treshold: 3.48"]
+	140363184493568 -> 140363184493696
+	140363184493824 [label="Is feature sulphates(f_id: 9) <= 0.5?
+sulphates
+Treshold: 0.5"]
+	140363184493696 -> 140363184493824
+	140363184493952 [label="Class: 6"]
+	140363184493824 -> 140363184493952
+	140363184494016 [label="Is feature residual sugar(f_id: 3) <= 1.5?
+residual sugar
+Treshold: 1.5"]
+	140363184493824 -> 140363184494016
+	140363184494144 [label="Class: 7"]
+	140363184494016 -> 140363184494144
+	140363184494208 [label="Class: 6"]
+	140363184494016 -> 140363184494208
+	140363184494272 [label="Class: 5"]
+	140363184493696 -> 140363184494272
+	140363184494336 [label="Is feature fixed acidity(f_id: 0) <= 6.8?
+fixed acidity
+Treshold: 6.8"]
+	140363184493568 -> 140363184494336
+	140363184494464 [label="Is feature chlorides(f_id: 4) <= 0.051?
+chlorides
+Treshold: 0.051"]
+	140363184494336 -> 140363184494464
+	140363184494592 [label="Is feature total sulfur dioxide(f_id: 6) <= 120.0?
+total sulfur dioxide
+Treshold: 120.0"]
+	140363184494464 -> 140363184494592
+	140363184494656 [label="Class: 5"]
+	140363184494592 -> 140363184494656
+	140363184494720 [label="Class: 7"]
+	140363184494592 -> 140363184494720
+	140363184494784 [label="Class: 6"]
+	140363184494464 -> 140363184494784
+	140363184494848 [label="Class: 5"]
+	140363184494336 -> 140363184494848
+	140363184494912 [label="Is feature density(f_id: 7) <= 0.9952?
+density
+Treshold: 0.9952"]
+	140363184493056 -> 140363184494912
+	140363184495040 [label="Is feature pH(f_id: 8) <= 2.89?
+pH
+Treshold: 2.89"]
+	140363184494912 -> 140363184495040
+	140363184495168 [label="Class: 6"]
+	140363184495040 -> 140363184495168
+	140363184495232 [label="Class: 4"]
+	140363184495040 -> 140363184495232
+	140363184495296 [label="Is feature chlorides(f_id: 4) <= 0.059?
+chlorides
+Treshold: 0.059"]
+	140363184494912 -> 140363184495296
+	140363184495424 [label="Is feature residual sugar(f_id: 3) <= 10.7?
+residual sugar
+Treshold: 10.7"]
+	140363184495296 -> 140363184495424
+	140363184495552 [label="Is feature density(f_id: 7) <= 0.9959?
+density
+Treshold: 0.9959"]
+	140363184495424 -> 140363184495552
+	140363184495680 [label="Class: 5"]
+	140363184495552 -> 140363184495680
+	140363184495744 [label="Is feature alcohol(f_id: 10) <= 10.4?
+alcohol
+Treshold: 10.4"]
+	140363184495552 -> 140363184495744
+	140363184495872 [label="Class: 4"]
+	140363184495744 -> 140363184495872
+	140363184495936 [label="Class: 5"]
+	140363184495744 -> 140363184495936
+	140363184496000 [label="Is feature free sulfur dioxide(f_id: 5) <= 19.0?
+free sulfur dioxide
+Treshold: 19.0"]
+	140363184495424 -> 140363184496000
+	140363184496064 [label="Class: 6"]
+	140363184496000 -> 140363184496064
+	140363184496128 [label="Class: 5"]
+	140363184496000 -> 140363184496128
+	140363184496192 [label="Class: 6"]
+	140363184495296 -> 140363184496192
+	140363184496256 [label="Is feature total sulfur dioxide(f_id: 6) <= 160.0?
+total sulfur dioxide
+Treshold: 160.0"]
+	140363184490816 -> 140363184496256
+	140363184496320 [label="Is feature pH(f_id: 8) <= 3.32?
+pH
+Treshold: 3.32"]
+	140363184496256 -> 140363184496320
+	140363184496448 [label="Is feature fixed acidity(f_id: 0) <= 7.1?
+fixed acidity
+Treshold: 7.1"]
+	140363184496320 -> 140363184496448
+	140363184496576 [label="Is feature density(f_id: 7) <= 0.99344?
+density
+Treshold: 0.99344"]
+	140363184496448 -> 140363184496576
+	140363184496704 [label="Is feature total sulfur dioxide(f_id: 6) <= 133.0?
+total sulfur dioxide
+Treshold: 133.0"]
+	140363184496576 -> 140363184496704
+	140363184496768 [label="Is feature density(f_id: 7) <= 0.9923?
+density
+Treshold: 0.9923"]
+	140363184496704 -> 140363184496768
+	140363184496896 [label="Is feature volatile acidity(f_id: 1) <= 0.3?
+volatile acidity
+Treshold: 0.3"]
+	140363184496768 -> 140363184496896
+	140363184496960 [label="Is feature fixed acidity(f_id: 0) <= 6.0?
+fixed acidity
+Treshold: 6.0"]
+	140363184496896 -> 140363184496960
+	140363184497088 [label="Class: 6"]
+	140363184496960 -> 140363184497088
+	140363184497152 [label="Class: 5"]
+	140363184496960 -> 140363184497152
+	140363184497216 [label="Class: 6"]
+	140363184496896 -> 140363184497216
+	140363184497280 [label="Is feature citric acid(f_id: 2) <= 0.04?
+citric acid
+Treshold: 0.04"]
+	140363184496768 -> 140363184497280
+	140363184497408 [label="Class: 4"]
+	140363184497280 -> 140363184497408
+	140363184497472 [label="Is feature free sulfur dioxide(f_id: 5) <= 26.0?
+free sulfur dioxide
+Treshold: 26.0"]
+	140363184497280 -> 140363184497472
+	140363184497536 [label="Class: 6"]
+	140363184497472 -> 140363184497536
+	140363184497600 [label="Is feature residual sugar(f_id: 3) <= 1.6?
+residual sugar
+Treshold: 1.6"]
+	140363184497472 -> 140363184497600
+	140363184497728 [label="Class: 6"]
+	140363184497600 -> 140363184497728
+	140363184497792 [label="Class: 4"]
+	140363184497600 -> 140363184497792
+	140363184497856 [label="Is feature pH(f_id: 8) <= 2.95?
+pH
+Treshold: 2.95"]
+	140363184496704 -> 140363184497856
+	140363184497984 [label="Class: 6"]
+	140363184497856 -> 140363184497984
+	140363184498048 [label="Class: 5"]
+	140363184497856 -> 140363184498048
+	140363184498112 [label="Is feature citric acid(f_id: 2) <= 0.19?
+citric acid
+Treshold: 0.19"]
+	140363184496576 -> 140363184498112
+	140363184498240 [label="Is feature sulphates(f_id: 9) <= 0.4?
+sulphates
+Treshold: 0.4"]
+	140363184498112 -> 140363184498240
+	140363184498368 [label="Class: 6"]
+	140363184498240 -> 140363184498368
+	140363184498432 [label="Class: 5"]
+	140363184498240 -> 140363184498432
+	140363184498496 [label="Class: 6"]
+	140363184498112 -> 140363184498496
+	140363184498560 [label="Is feature pH(f_id: 8) <= 3.18?
+pH
+Treshold: 3.18"]
+	140363184496448 -> 140363184498560
+	140363184498688 [label="Is feature fixed acidity(f_id: 0) <= 7.2?
+fixed acidity
+Treshold: 7.2"]
+	140363184498560 -> 140363184498688
+	140363184498816 [label="Class: 7"]
+	140363184498688 -> 140363184498816
+	140363184498880 [label="Is feature sulphates(f_id: 9) <= 0.45?
+sulphates
+Treshold: 0.45"]
+	140363184498688 -> 140363184498880
+	140363184499008 [label="Is feature total sulfur dioxide(f_id: 6) <= 124.0?
+total sulfur dioxide
+Treshold: 124.0"]
+	140363184498880 -> 140363184499008
+	140363184499072 [label="Is feature alcohol(f_id: 10) <= 10.3?
+alcohol
+Treshold: 10.3"]
+	140363184499008 -> 140363184499072
+	140363184499200 [label="Class: 7"]
+	140363184499072 -> 140363184499200
+	140363184499264 [label="Class: 6"]
+	140363184499072 -> 140363184499264
+	140363184499328 [label="Is feature free sulfur dioxide(f_id: 5) <= 28.0?
+free sulfur dioxide
+Treshold: 28.0"]
+	140363184499008 -> 140363184499328
+	140363184499392 [label="Is feature pH(f_id: 8) <= 3.12?
+pH
+Treshold: 3.12"]
+	140363184499328 -> 140363184499392
+	140363184499520 [label="Class: 6"]
+	140363184499392 -> 140363184499520
+	140363184499584 [label="Class: 5"]
+	140363184499392 -> 140363184499584
+	140363184499648 [label="Is feature volatile acidity(f_id: 1) <= 0.28?
+volatile acidity
+Treshold: 0.28"]
+	140363184499328 -> 140363184499648
+	140363184499712 [label="Class: 4"]
+	140363184499648 -> 140363184499712
+	140363184499776 [label="Class: 5"]
+	140363184499648 -> 140363184499776
+	140363184499840 [label="Is feature free sulfur dioxide(f_id: 5) <= 33.0?
+free sulfur dioxide
+Treshold: 33.0"]
+	140363184498880 -> 140363184499840
+	140363184499904 [label="Is feature fixed acidity(f_id: 0) <= 7.8?
+fixed acidity
+Treshold: 7.8"]
+	140363184499840 -> 140363184499904
+	140363184500032 [label="Class: 5"]
+	140363184499904 -> 140363184500032
+	140363184500096 [label="Is feature sulphates(f_id: 9) <= 0.51?
+sulphates
+Treshold: 0.51"]
+	140363184499904 -> 140363184500096
+	140363184500224 [label="Class: 6"]
+	140363184500096 -> 140363184500224
+	140363184500288 [label="Class: 5"]
+	140363184500096 -> 140363184500288
+	140363184500352 [label="Is feature free sulfur dioxide(f_id: 5) <= 43.0?
+free sulfur dioxide
+Treshold: 43.0"]
+	140363184499840 -> 140363184500352
+	140363184500416 [label="Class: 6"]
+	140363184500352 -> 140363184500416
+	140363184500480 [label="Is feature fixed acidity(f_id: 0) <= 8.1?
+fixed acidity
+Treshold: 8.1"]
+	140363184500352 -> 140363184500480
+	140363184500608 [label="Class: 7"]
+	140363184500480 -> 140363184500608
+	140363184500672 [label="Class: 5"]
+	140363184500480 -> 140363184500672
+	140363184500736 [label="Is feature volatile acidity(f_id: 1) <= 0.27?
+volatile acidity
+Treshold: 0.27"]
+	140363184498560 -> 140363184500736
+	140363184500800 [label="Is feature sulphates(f_id: 9) <= 0.46?
+sulphates
+Treshold: 0.46"]
+	140363184500736 -> 140363184500800
+	140363184500928 [label="Class: 9"]
+	140363184500800 -> 140363184500928
+	140363184500992 [label="Class: 8"]
+	140363184500800 -> 140363184500992
+	140363184501056 [label="Class: 6"]
+	140363184500736 -> 140363184501056
+	140363184501120 [label="Is feature free sulfur dioxide(f_id: 5) <= 31.0?
+free sulfur dioxide
+Treshold: 31.0"]
+	140363184496320 -> 140363184501120
+	140363184501184 [label="Is feature citric acid(f_id: 2) <= 0.33?
+citric acid
+Treshold: 0.33"]
+	140363184501120 -> 140363184501184
+	140363184501312 [label="Is feature sulphates(f_id: 9) <= 0.6?
+sulphates
+Treshold: 0.6"]
+	140363184501184 -> 140363184501312
+	140363184501440 [label="Is feature total sulfur dioxide(f_id: 6) <= 156.0?
+total sulfur dioxide
+Treshold: 156.0"]
+	140363184501312 -> 140363184501440
+	140363184501504 [label="Class: 6"]
+	140363184501440 -> 140363184501504
+	140363184501568 [label="Class: 7"]
+	140363184501440 -> 140363184501568
+	140363184501632 [label="Class: 7"]
+	140363184501312 -> 140363184501632
+	140363184501696 [label="Class: 5"]
+	140363184501184 -> 140363184501696
+	140363184501760 [label="Is feature total sulfur dioxide(f_id: 6) <= 111.0?
+total sulfur dioxide
+Treshold: 111.0"]
+	140363184501120 -> 140363184501760
+	140363184501824 [label="Class: 6"]
+	140363184501760 -> 140363184501824
+	140363184501888 [label="Is feature density(f_id: 7) <= 0.9951?
+density
+Treshold: 0.9951"]
+	140363184501760 -> 140363184501888
+	140363184502016 [label="Is feature fixed acidity(f_id: 0) <= 6.9?
+fixed acidity
+Treshold: 6.9"]
+	140363184501888 -> 140363184502016
+	140363184502144 [label="Class: 7"]
+	140363184502016 -> 140363184502144
+	140363184502208 [label="Is feature density(f_id: 7) <= 0.9934?
+density
+Treshold: 0.9934"]
+	140363184502016 -> 140363184502208
+	140363184502336 [label="Class: 8"]
+	140363184502208 -> 140363184502336
+	140363184502400 [label="Class: 7"]
+	140363184502208 -> 140363184502400
+	140363184502464 [label="Class: 6"]
+	140363184501888 -> 140363184502464
+	140363184502528 [label="Is feature volatile acidity(f_id: 1) <= 0.57?
+volatile acidity
+Treshold: 0.57"]
+	140363184496256 -> 140363184502528
+	140363184502592 [label="Is feature citric acid(f_id: 2) <= 0.3?
+citric acid
+Treshold: 0.3"]
+	140363184502528 -> 140363184502592
+	140363184502720 [label="Is feature density(f_id: 7) <= 0.99212?
+density
+Treshold: 0.99212"]
+	140363184502592 -> 140363184502720
+	140363184502848 [label="Class: 5"]
+	140363184502720 -> 140363184502848
+	140363184502912 [label="Is feature free sulfur dioxide(f_id: 5) <= 51.0?
+free sulfur dioxide
+Treshold: 51.0"]
+	140363184502720 -> 140363184502912
+	140363184502976 [label="Is feature pH(f_id: 8) <= 3.15?
+pH
+Treshold: 3.15"]
+	140363184502912 -> 140363184502976
+	140363184503104 [label="Class: 6"]
+	140363184502976 -> 140363184503104
+	140363184503168 [label="Is feature chlorides(f_id: 4) <= 0.043?
+chlorides
+Treshold: 0.043"]
+	140363184502976 -> 140363184503168
+	140363184503296 [label="Class: 6"]
+	140363184503168 -> 140363184503296
+	140363184503360 [label="Is feature sulphates(f_id: 9) <= 0.54?
+sulphates
+Treshold: 0.54"]
+	140363184503168 -> 140363184503360
+	140363184503488 [label="Is feature volatile acidity(f_id: 1) <= 0.29?
+volatile acidity
+Treshold: 0.29"]
+	140363184503360 -> 140363184503488
+	140363184503552 [label="Class: 5"]
+	140363184503488 -> 140363184503552
+	140363184503616 [label="Is feature fixed acidity(f_id: 0) <= 6.6?
+fixed acidity
+Treshold: 6.6"]
+	140363184503488 -> 140363184503616
+	140363184503744 [label="Class: 6"]
+	140363184503616 -> 140363184503744
+	140363184503808 [label="Class: 5"]
+	140363184503616 -> 140363184503808
+	140363184503872 [label="Class: 5"]
+	140363184503360 -> 140363184503872
+	140363184503936 [label="Class: 6"]
+	140363184502912 -> 140363184503936
+	140363184504000 [label="Is feature density(f_id: 7) <= 0.99513?
+density
+Treshold: 0.99513"]
+	140363184502592 -> 140363184504000
+	140363184504128 [label="Is feature citric acid(f_id: 2) <= 0.34?
+citric acid
+Treshold: 0.34"]
+	140363184504000 -> 140363184504128
+	140363184504256 [label="Is feature volatile acidity(f_id: 1) <= 0.3?
+volatile acidity
+Treshold: 0.3"]
+	140363184504128 -> 140363184504256
+	140363184504320 [label="Is feature residual sugar(f_id: 3) <= 5.8?
+residual sugar
+Treshold: 5.8"]
+	140363184504256 -> 140363184504320
+	140363184504448 [label="Class: 5"]
+	140363184504320 -> 140363184504448
+	140363184504512 [label="Class: 7"]
+	140363184504320 -> 140363184504512
+	140363184504576 [label="Class: 6"]
+	140363184504256 -> 140363184504576
+	140363184504640 [label="Is feature citric acid(f_id: 2) <= 0.37?
+citric acid
+Treshold: 0.37"]
+	140363184504128 -> 140363184504640
+	140363184504768 [label="Class: 4"]
+	140363184504640 -> 140363184504768
+	140363184554048 [label="Is feature pH(f_id: 8) <= 3.25?
+pH
+Treshold: 3.25"]
+	140363184504640 -> 140363184554048
+	140363184554176 [label="Class: 6"]
+	140363184554048 -> 140363184554176
+	140363184554240 [label="Class: 7"]
+	140363184554048 -> 140363184554240
+	140363184554304 [label="Is feature total sulfur dioxide(f_id: 6) <= 227.0?
+total sulfur dioxide
+Treshold: 227.0"]
+	140363184504000 -> 140363184554304
+	140363184554368 [label="Is feature alcohol(f_id: 10) <= 10.1?
+alcohol
+Treshold: 10.1"]
+	140363184554304 -> 140363184554368
+	140363184554496 [label="Class: 6"]
+	140363184554368 -> 140363184554496
+	140363184554560 [label="Is feature citric acid(f_id: 2) <= 0.44?
+citric acid
+Treshold: 0.44"]
+	140363184554368 -> 140363184554560
+	140363184554688 [label="Class: 5"]
+	140363184554560 -> 140363184554688
+	140363184554752 [label="Class: 6"]
+	140363184554560 -> 140363184554752
+	140363184554816 [label="Class: 6"]
+	140363184554304 -> 140363184554816
+	140363184554880 [label="Is feature citric acid(f_id: 2) <= 0.46?
+citric acid
+Treshold: 0.46"]
+	140363184502528 -> 140363184554880
+	140363184555008 [label="Class: 4"]
+	140363184554880 -> 140363184555008
+	140363184555072 [label="Class: 5"]
+	140363184554880 -> 140363184555072
+	140363184555136 [label="Is feature free sulfur dioxide(f_id: 5) <= 11.0?
+free sulfur dioxide
+Treshold: 11.0"]
+	140363292071296 -> 140363184555136
+	140363184555200 [label="Is feature citric acid(f_id: 2) <= 0.24?
+citric acid
+Treshold: 0.24"]
+	140363184555136 -> 140363184555200
+	140363184555328 [label="Is feature density(f_id: 7) <= 0.99076?
+density
+Treshold: 0.99076"]
+	140363184555200 -> 140363184555328
+	140363184555456 [label="Is feature sulphates(f_id: 9) <= 0.47?
+sulphates
+Treshold: 0.47"]
+	140363184555328 -> 140363184555456
+	140363184555584 [label="Class: 4"]
+	140363184555456 -> 140363184555584
+	140363184555648 [label="Class: 7"]
+	140363184555456 -> 140363184555648
+	140363184555712 [label="Is feature residual sugar(f_id: 3) <= 4.2?
+residual sugar
+Treshold: 4.2"]
+	140363184555328 -> 140363184555712
+	140363184555840 [label="Is feature chlorides(f_id: 4) <= 0.042?
+chlorides
+Treshold: 0.042"]
+	140363184555712 -> 140363184555840
+	140363184555968 [label="Class: 4"]
+	140363184555840 -> 140363184555968
+	140363184556032 [label="Class: 5"]
+	140363184555840 -> 140363184556032
+	140363184556096 [label="Class: 5"]
+	140363184555712 -> 140363184556096
+	140363184556160 [label="Is feature free sulfur dioxide(f_id: 5) <= 7.0?
+free sulfur dioxide
+Treshold: 7.0"]
+	140363184555200 -> 140363184556160
+	140363184556224 [label="Is feature pH(f_id: 8) <= 3.27?
+pH
+Treshold: 3.27"]
+	140363184556160 -> 140363184556224
+	140363184556352 [label="Is feature pH(f_id: 8) <= 3.08?
+pH
+Treshold: 3.08"]
+	140363184556224 -> 140363184556352
+	140363184556480 [label="Is feature alcohol(f_id: 10) <= 12.5?
+alcohol
+Treshold: 12.5"]
+	140363184556352 -> 140363184556480
+	140363184556608 [label="Is feature density(f_id: 7) <= 0.9906?
+density
+Treshold: 0.9906"]
+	140363184556480 -> 140363184556608
+	140363184556736 [label="Class: 4"]
+	140363184556608 -> 140363184556736
+	140363184556800 [label="Is feature pH(f_id: 8) <= 3.02?
+pH
+Treshold: 3.02"]
+	140363184556608 -> 140363184556800
+	140363184556928 [label="Is feature citric acid(f_id: 2) <= 0.31?
+citric acid
+Treshold: 0.31"]
+	140363184556800 -> 140363184556928
+	140363184557056 [label="Class: 5"]
+	140363184556928 -> 140363184557056
+	140363184557120 [label="Class: 6"]
+	140363184556928 -> 140363184557120
+	140363184557184 [label="Class: 8"]
+	140363184556800 -> 140363184557184
+	140363184557248 [label="Class: 6"]
+	140363184556480 -> 140363184557248
+	140363184557312 [label="Is feature total sulfur dioxide(f_id: 6) <= 89.0?
+total sulfur dioxide
+Treshold: 89.0"]
+	140363184556352 -> 140363184557312
+	140363184557376 [label="Is feature citric acid(f_id: 2) <= 0.31?
+citric acid
+Treshold: 0.31"]
+	140363184557312 -> 140363184557376
+	140363184557504 [label="Class: 6"]
+	140363184557376 -> 140363184557504
+	140363184557568 [label="Is feature sulphates(f_id: 9) <= 0.52?
+sulphates
+Treshold: 0.52"]
+	140363184557376 -> 140363184557568
+	140363184557696 [label="Is feature volatile acidity(f_id: 1) <= 0.34?
+volatile acidity
+Treshold: 0.34"]
+	140363184557568 -> 140363184557696
+	140363184557760 [label="Class: 5"]
+	140363184557696 -> 140363184557760
+	140363184557824 [label="Class: 6"]
+	140363184557696 -> 140363184557824
+	140363184557888 [label="Class: 6"]
+	140363184557568 -> 140363184557888
+	140363184557952 [label="Is feature chlorides(f_id: 4) <= 0.045?
+chlorides
+Treshold: 0.045"]
+	140363184557312 -> 140363184557952
+	140363184558080 [label="Class: 4"]
+	140363184557952 -> 140363184558080
+	140363184558144 [label="Class: 6"]
+	140363184557952 -> 140363184558144
+	140363184558208 [label="Is feature citric acid(f_id: 2) <= 0.38?
+citric acid
+Treshold: 0.38"]
+	140363184556224 -> 140363184558208
+	140363184558336 [label="Is feature pH(f_id: 8) <= 3.3?
+pH
+Treshold: 3.3"]
+	140363184558208 -> 140363184558336
+	140363184558464 [label="Class: 4"]
+	140363184558336 -> 140363184558464
+	140363184558528 [label="Class: 5"]
+	140363184558336 -> 140363184558528
+	140363184558592 [label="Class: 3"]
+	140363184558208 -> 140363184558592
+	140363184558656 [label="Is feature alcohol(f_id: 10) <= 12.1?
+alcohol
+Treshold: 12.1"]
+	140363184556160 -> 140363184558656
+	140363184558784 [label="Is feature free sulfur dioxide(f_id: 5) <= 9.0?
+free sulfur dioxide
+Treshold: 9.0"]
+	140363184558656 -> 140363184558784
+	140363184558848 [label="Is feature alcohol(f_id: 10) <= 11.0?
+alcohol
+Treshold: 11.0"]
+	140363184558784 -> 140363184558848
+	140363184558976 [label="Class: 7"]
+	140363184558848 -> 140363184558976
+	140363184559040 [label="Is feature residual sugar(f_id: 3) <= 3.6?
+residual sugar
+Treshold: 3.6"]
+	140363184558848 -> 140363184559040
+	140363184559168 [label="Is feature free sulfur dioxide(f_id: 5) <= 8.0?
+free sulfur dioxide
+Treshold: 8.0"]
+	140363184559040 -> 140363184559168
+	140363184559232 [label="Class: 6"]
+	140363184559168 -> 140363184559232
+	140363184559296 [label="Class: 4"]
+	140363184559168 -> 140363184559296
+	140363184559360 [label="Is feature alcohol(f_id: 10) <= 11.7?
+alcohol
+Treshold: 11.7"]
+	140363184559040 -> 140363184559360
+	140363184559488 [label="Class: 6"]
+	140363184559360 -> 140363184559488
+	140363184559552 [label="Is feature fixed acidity(f_id: 0) <= 6.6?
+fixed acidity
+Treshold: 6.6"]
+	140363184559360 -> 140363184559552
+	140363184559680 [label="Class: 5"]
+	140363184559552 -> 140363184559680
+	140363184559744 [label="Class: 7"]
+	140363184559552 -> 140363184559744
+	140363184559808 [label="Is feature citric acid(f_id: 2) <= 0.31?
+citric acid
+Treshold: 0.31"]
+	140363184558784 -> 140363184559808
+	140363184559936 [label="Is feature density(f_id: 7) <= 0.99202?
+density
+Treshold: 0.99202"]
+	140363184559808 -> 140363184559936
+	140363184560064 [label="Class: 6"]
+	140363184559936 -> 140363184560064
+	140363184560128 [label="Class: 4"]
+	140363184559936 -> 140363184560128
+	140363184560192 [label="Is feature total sulfur dioxide(f_id: 6) <= 80.0?
+total sulfur dioxide
+Treshold: 80.0"]
+	140363184559808 -> 140363184560192
+	140363184560256 [label="Class: 5"]
+	140363184560192 -> 140363184560256
+	140363184560320 [label="Is feature volatile acidity(f_id: 1) <= 0.28?
+volatile acidity
+Treshold: 0.28"]
+	140363184560192 -> 140363184560320
+	140363184560384 [label="Class: 6"]
+	140363184560320 -> 140363184560384
+	140363184560448 [label="Is feature pH(f_id: 8) <= 3.25?
+pH
+Treshold: 3.25"]
+	140363184560320 -> 140363184560448
+	140363184560576 [label="Class: 5"]
+	140363184560448 -> 140363184560576
+	140363184560640 [label="Class: 6"]
+	140363184560448 -> 140363184560640
+	140363184560704 [label="Is feature chlorides(f_id: 4) <= 0.027?
+chlorides
+Treshold: 0.027"]
+	140363184558656 -> 140363184560704
+	140363184560832 [label="Class: 7"]
+	140363184560704 -> 140363184560832
+	140363184560896 [label="Is feature total sulfur dioxide(f_id: 6) <= 34.0?
+total sulfur dioxide
+Treshold: 34.0"]
+	140363184560704 -> 140363184560896
+	140363184560960 [label="Class: 7"]
+	140363184560896 -> 140363184560960
+	140363184561024 [label="Class: 6"]
+	140363184560896 -> 140363184561024
+	140363184561088 [label="Is feature alcohol(f_id: 10) <= 12.0666666666667?
+alcohol
+Treshold: 12.0666666666667"]
+	140363184555136 -> 140363184561088
+	140363184561216 [label="Is feature free sulfur dioxide(f_id: 5) <= 73.0?
+free sulfur dioxide
+Treshold: 73.0"]
+	140363184561088 -> 140363184561216
+	140363184561280 [label="Is feature fixed acidity(f_id: 0) <= 6.7?
+fixed acidity
+Treshold: 6.7"]
+	140363184561216 -> 140363184561280
+	140363184561408 [label="Is feature residual sugar(f_id: 3) <= 0.95?
+residual sugar
+Treshold: 0.95"]
+	140363184561280 -> 140363184561408
+	140363184561536 [label="Is feature density(f_id: 7) <= 0.98936?
+density
+Treshold: 0.98936"]
+	140363184561408 -> 140363184561536
+	140363184561664 [label="Class: 7"]
+	140363184561536 -> 140363184561664
+	140363184561728 [label="Is feature chlorides(f_id: 4) <= 0.045?
+chlorides
+Treshold: 0.045"]
+	140363184561536 -> 140363184561728
+	140363184561856 [label="Is feature citric acid(f_id: 2) <= 0.27?
+citric acid
+Treshold: 0.27"]
+	140363184561728 -> 140363184561856
+	140363184561984 [label="Class: 5"]
+	140363184561856 -> 140363184561984
+	140363184562048 [label="Is feature total sulfur dioxide(f_id: 6) <= 98.0?
+total sulfur dioxide
+Treshold: 98.0"]
+	140363184561856 -> 140363184562048
+	140363184562112 [label="Class: 6"]
+	140363184562048 -> 140363184562112
+	140363184562176 [label="Class: 5"]
+	140363184562048 -> 140363184562176
+	140363184562240 [label="Class: 3"]
+	140363184561728 -> 140363184562240
+	140363184562304 [label="Is feature pH(f_id: 8) <= 3.05?
+pH
+Treshold: 3.05"]
+	140363184561408 -> 140363184562304
+	140363184562432 [label="Is feature citric acid(f_id: 2) <= 0.24?
+citric acid
+Treshold: 0.24"]
+	140363184562304 -> 140363184562432
+	140363184562560 [label="Is feature sulphates(f_id: 9) <= 0.34?
+sulphates
+Treshold: 0.34"]
+	140363184562432 -> 140363184562560
+	140363184562688 [label="Class: 6"]
+	140363184562560 -> 140363184562688
+	140363184562752 [label="Class: 5"]
+	140363184562560 -> 140363184562752
+	140363184562816 [label="Is feature pH(f_id: 8) <= 3.0?
+pH
+Treshold: 3.0"]
+	140363184562432 -> 140363184562816
+	140363184562944 [label="Is feature free sulfur dioxide(f_id: 5) <= 32.0?
+free sulfur dioxide
+Treshold: 32.0"]
+	140363184562816 -> 140363184562944
+	140363184563008 [label="Class: 6"]
+	140363184562944 -> 140363184563008
+	140363184563072 [label="Is feature density(f_id: 7) <= 0.9905?
+density
+Treshold: 0.9905"]
+	140363184562944 -> 140363184563072
+	140363184563200 [label="Is feature free sulfur dioxide(f_id: 5) <= 45.0?
+free sulfur dioxide
+Treshold: 45.0"]
+	140363184563072 -> 140363184563200
+	140363184563264 [label="Class: 6"]
+	140363184563200 -> 140363184563264
+	140363184563328 [label="Class: 7"]
+	140363184563200 -> 140363184563328
+	140363184563392 [label="Class: 7"]
+	140363184563072 -> 140363184563392
+	140363184563456 [label="Class: 6"]
+	140363184562816 -> 140363184563456
+	140363184563520 [label="Is feature fixed acidity(f_id: 0) <= 6.3?
+fixed acidity
+Treshold: 6.3"]
+	140363184562304 -> 140363184563520
+	140363184563648 [label="Is feature chlorides(f_id: 4) <= 0.029?
+chlorides
+Treshold: 0.029"]
+	140363184563520 -> 140363184563648
+	140363184563776 [label="Is feature fixed acidity(f_id: 0) <= 5.9?
+fixed acidity
+Treshold: 5.9"]
+	140363184563648 -> 140363184563776
+	140363184563904 [label="Is feature total sulfur dioxide(f_id: 6) <= 128.0?
+total sulfur dioxide
+Treshold: 128.0"]
+	140363184563776 -> 140363184563904
+	140363184563968 [label="Is feature residual sugar(f_id: 3) <= 1.4?
+residual sugar
+Treshold: 1.4"]
+	140363184563904 -> 140363184563968
+	140363184564096 [label="Class: 6"]
+	140363184563968 -> 140363184564096
+	140363184564160 [label="Is feature chlorides(f_id: 4) <= 0.026?
+chlorides
+Treshold: 0.026"]
+	140363184563968 -> 140363184564160
+	140363184564288 [label="Is feature residual sugar(f_id: 3) <= 1.55?
+residual sugar
+Treshold: 1.55"]
+	140363184564160 -> 140363184564288
+	140363184564416 [label="Class: 8"]
+	140363184564288 -> 140363184564416
+	140363184564480 [label="Class: 6"]
+	140363184564288 -> 140363184564480
+	140363184564544 [label="Is feature volatile acidity(f_id: 1) <= 0.3?
+volatile acidity
+Treshold: 0.3"]
+	140363184564160 -> 140363184564544
+	140363184564608 [label="Class: 8"]
+	140363184564544 -> 140363184564608
+	140363184564672 [label="Class: 6"]
+	140363184564544 -> 140363184564672
+	140363184564736 [label="Is feature volatile acidity(f_id: 1) <= 0.33?
+volatile acidity
+Treshold: 0.33"]
+	140363184563904 -> 140363184564736
+	140363184564800 [label="Class: 7"]
+	140363184564736 -> 140363184564800
+	140363184564864 [label="Class: 6"]
+	140363184564736 -> 140363184564864
+	140363184564928 [label="Is feature pH(f_id: 8) <= 3.38?
+pH
+Treshold: 3.38"]
+	140363184563776 -> 140363184564928
+	140363184565056 [label="Is feature total sulfur dioxide(f_id: 6) <= 152.0?
+total sulfur dioxide
+Treshold: 152.0"]
+	140363184564928 -> 140363184565056
+	140363184565120 [label="Is feature citric acid(f_id: 2) <= 0.25?
+citric acid
+Treshold: 0.25"]
+	140363184565056 -> 140363184565120
+	140363184565248 [label="Class: 6"]
+	140363184565120 -> 140363184565248
+	140363184565312 [label="Class: 7"]
+	140363184565120 -> 140363184565312
+	140363184565376 [label="Class: 6"]
+	140363184565056 -> 140363184565376
+	140363184565440 [label="Is feature pH(f_id: 8) <= 3.39?
+pH
+Treshold: 3.39"]
+	140363184564928 -> 140363184565440
+	140363184565568 [label="Class: 5"]
+	140363184565440 -> 140363184565568
+	140363184565632 [label="Class: 8"]
+	140363184565440 -> 140363184565632
+	140363184565696 [label="Is feature free sulfur dioxide(f_id: 5) <= 60.0?
+free sulfur dioxide
+Treshold: 60.0"]
+	140363184563648 -> 140363184565696
+	140363184565760 [label="Is feature alcohol(f_id: 10) <= 11.45?
+alcohol
+Treshold: 11.45"]
+	140363184565696 -> 140363184565760
+	140363184565888 [label="Is feature free sulfur dioxide(f_id: 5) <= 14.0?
+free sulfur dioxide
+Treshold: 14.0"]
+	140363184565760 -> 140363184565888
+	140363184565952 [label="Is feature fixed acidity(f_id: 0) <= 5.8?
+fixed acidity
+Treshold: 5.8"]
+	140363184565888 -> 140363184565952
+	140363184566080 [label="Class: 7"]
+	140363184565952 -> 140363184566080
+	140363184566144 [label="Class: 8"]
+	140363184565952 -> 140363184566144
+	140363184566208 [label="Is feature residual sugar(f_id: 3) <= 10.7?
+residual sugar
+Treshold: 10.7"]
+	140363184565888 -> 140363184566208
+	140363184566336 [label="Is feature residual sugar(f_id: 3) <= 4.8?
+residual sugar
+Treshold: 4.8"]
+	140363184566208 -> 140363184566336
+	140363184566464 [label="Is feature volatile acidity(f_id: 1) <= 0.23?
+volatile acidity
+Treshold: 0.23"]
+	140363184566336 -> 140363184566464
+	140363184566528 [label="Is feature free sulfur dioxide(f_id: 5) <= 57.0?
+free sulfur dioxide
+Treshold: 57.0"]
+	140363184566464 -> 140363184566528
+	140363184566592 [label="Is feature volatile acidity(f_id: 1) <= 0.12?
+volatile acidity
+Treshold: 0.12"]
+	140363184566528 -> 140363184566592
+	140363184566656 [label="Class: 7"]
+	140363184566592 -> 140363184566656
+	140363184566720 [label="Is feature chlorides(f_id: 4) <= 0.051?
+chlorides
+Treshold: 0.051"]
+	140363184566592 -> 140363184566720
+	140363184566848 [label="Is feature total sulfur dioxide(f_id: 6) <= 110.0?
+total sulfur dioxide
+Treshold: 110.0"]
+	140363184566720 -> 140363184566848
+	140363184566912 [label="Class: 6"]
+	140363184566848 -> 140363184566912
+	140363184566976 [label="Is feature residual sugar(f_id: 3) <= 1.1?
+residual sugar
+Treshold: 1.1"]
+	140363184566848 -> 140363184566976
+	140363184567104 [label="Class: 7"]
+	140363184566976 -> 140363184567104
+	140363184567168 [label="Is feature fixed acidity(f_id: 0) <= 4.8?
+fixed acidity
+Treshold: 4.8"]
+	140363184566976 -> 140363184567168
+	140363184567296 [label="Class: 7"]
+	140363184567168 -> 140363184567296
+	140363184567360 [label="Is feature pH(f_id: 8) <= 3.16?
+pH
+Treshold: 3.16"]
+	140363184567168 -> 140363184567360
+	140363184567488 [label="Is feature sulphates(f_id: 9) <= 0.42?
+sulphates
+Treshold: 0.42"]
+	140363184567360 -> 140363184567488
+	140363184567616 [label="Class: 7"]
+	140363184567488 -> 140363184567616
+	140363184567680 [label="Class: 6"]
+	140363184567488 -> 140363184567680
+	140363184567744 [label="Class: 6"]
+	140363184567360 -> 140363184567744
+	140363184567808 [label="Class: 7"]
+	140363184566720 -> 140363184567808
+	140363184567872 [label="Class: 5"]
+	140363184566528 -> 140363184567872
+	140363184567936 [label="Is feature total sulfur dioxide(f_id: 6) <= 92.0?
+total sulfur dioxide
+Treshold: 92.0"]
+	140363184566464 -> 140363184567936
+	140363184568000 [label="Is feature total sulfur dioxide(f_id: 6) <= 77.0?
+total sulfur dioxide
+Treshold: 77.0"]
+	140363184567936 -> 140363184568000
+	140363184568064 [label="Is feature citric acid(f_id: 2) <= 0.1?
+citric acid
+Treshold: 0.1"]
+	140363184568000 -> 140363184568064
+	140363184568192 [label="Class: 7"]
+	140363184568064 -> 140363184568192
+	140363184568256 [label="Class: 5"]
+	140363184568064 -> 140363184568256
+	140363184568320 [label="Class: 7"]
+	140363184568000 -> 140363184568320
+	140363184568384 [label="Is feature sulphates(f_id: 9) <= 0.52?
+sulphates
+Treshold: 0.52"]
+	140363184567936 -> 140363184568384
+	140363184568512 [label="Is feature total sulfur dioxide(f_id: 6) <= 138.0?
+total sulfur dioxide
+Treshold: 138.0"]
+	140363184568384 -> 140363184568512
+	140363184568576 [label="Is feature density(f_id: 7) <= 0.99174?
+density
+Treshold: 0.99174"]
+	140363184568512 -> 140363184568576
+	140363184568704 [label="Is feature pH(f_id: 8) <= 3.18?
+pH
+Treshold: 3.18"]
+	140363184568576 -> 140363184568704
+	140363184568832 [label="Class: 6"]
+	140363184568704 -> 140363184568832
+	140363184568896 [label="Is feature free sulfur dioxide(f_id: 5) <= 32.0?
+free sulfur dioxide
+Treshold: 32.0"]
+	140363184568704 -> 140363184568896
+	140363184568960 [label="Class: 6"]
+	140363184568896 -> 140363184568960
+	140363184569024 [label="Class: 7"]
+	140363184568896 -> 140363184569024
+	140363184569088 [label="Is feature volatile acidity(f_id: 1) <= 0.27?
+volatile acidity
+Treshold: 0.27"]
+	140363184568576 -> 140363184569088
+	140363184569152 [label="Class: 6"]
+	140363184569088 -> 140363184569152
+	140363184569216 [label="Class: 5"]
+	140363184569088 -> 140363184569216
+	140363184569280 [label="Is feature volatile acidity(f_id: 1) <= 0.31?
+volatile acidity
+Treshold: 0.31"]
+	140363184568512 -> 140363184569280
+	140363184569344 [label="Class: 6"]
+	140363184569280 -> 140363184569344
+	140363184569408 [label="Class: 5"]
+	140363184569280 -> 140363184569408
+	140363184569472 [label="Is feature density(f_id: 7) <= 0.9921?
+density
+Treshold: 0.9921"]
+	140363184568384 -> 140363184569472
+	140363184569600 [label="Class: 6"]
+	140363184569472 -> 140363184569600
+	140363184569664 [label="Is feature total sulfur dioxide(f_id: 6) <= 132.0?
+total sulfur dioxide
+Treshold: 132.0"]
+	140363184569472 -> 140363184569664
+	140363184569728 [label="Class: 5"]
+	140363184569664 -> 140363184569728
+	140363184569792 [label="Is feature free sulfur dioxide(f_id: 5) <= 46.0?
+free sulfur dioxide
+Treshold: 46.0"]
+	140363184569664 -> 140363184569792
+	140363184569856 [label="Class: 6"]
+	140363184569792 -> 140363184569856
+	140363184569920 [label="Class: 7"]
+	140363184569792 -> 140363184569920
+	140363184569984 [label="Class: 6"]
+	140363184566336 -> 140363184569984
+	140363184570048 [label="Class: 7"]
+	140363184566208 -> 140363184570048
+	140363184570112 [label="Is feature free sulfur dioxide(f_id: 5) <= 49.0?
+free sulfur dioxide
+Treshold: 49.0"]
+	140363184565760 -> 140363184570112
+	140363184570176 [label="Is feature volatile acidity(f_id: 1) <= 0.35?
+volatile acidity
+Treshold: 0.35"]
+	140363184570112 -> 140363184570176
+	140363184570240 [label="Is feature citric acid(f_id: 2) <= 0.3?
+citric acid
+Treshold: 0.3"]
+	140363184570176 -> 140363184570240
+	140363184668736 [label="Is feature residual sugar(f_id: 3) <= 2.2?
+residual sugar
+Treshold: 2.2"]
+	140363184570240 -> 140363184668736
+	140363184668864 [label="Class: 6"]
+	140363184668736 -> 140363184668864
+	140363184668928 [label="Is feature residual sugar(f_id: 3) <= 3.2?
+residual sugar
+Treshold: 3.2"]
+	140363184668736 -> 140363184668928
+	140363184669056 [label="Class: 7"]
+	140363184668928 -> 140363184669056
+	140363184669120 [label="Is feature chlorides(f_id: 4) <= 0.038?
+chlorides
+Treshold: 0.038"]
+	140363184668928 -> 140363184669120
+	140363184669248 [label="Is feature volatile acidity(f_id: 1) <= 0.31?
+volatile acidity
+Treshold: 0.31"]
+	140363184669120 -> 140363184669248
+	140363184669312 [label="Is feature alcohol(f_id: 10) <= 11.8?
+alcohol
+Treshold: 11.8"]
+	140363184669248 -> 140363184669312
+	140363184669440 [label="Class: 7"]
+	140363184669312 -> 140363184669440
+	140363184669504 [label="Class: 6"]
+	140363184669312 -> 140363184669504
+	140363184669568 [label="Class: 6"]
+	140363184669248 -> 140363184669568
+	140363184669632 [label="Class: 6"]
+	140363184669120 -> 140363184669632
+	140363184669696 [label="Is feature volatile acidity(f_id: 1) <= 0.24?
+volatile acidity
+Treshold: 0.24"]
+	140363184570240 -> 140363184669696
+	140363184669760 [label="Is feature fixed acidity(f_id: 0) <= 5.6?
+fixed acidity
+Treshold: 5.6"]
+	140363184669696 -> 140363184669760
+	140363184669888 [label="Class: 7"]
+	140363184669760 -> 140363184669888
+	140363184669952 [label="Is feature chlorides(f_id: 4) <= 0.036?
+chlorides
+Treshold: 0.036"]
+	140363184669760 -> 140363184669952
+	140363184670080 [label="Class: 6"]
+	140363184669952 -> 140363184670080
+	140363184670144 [label="Is feature density(f_id: 7) <= 0.99051?
+density
+Treshold: 0.99051"]
+	140363184669952 -> 140363184670144
+	140363184670272 [label="Class: 7"]
+	140363184670144 -> 140363184670272
+	140363184670336 [label="Is feature alcohol(f_id: 10) <= 11.5?
+alcohol
+Treshold: 11.5"]
+	140363184670144 -> 140363184670336
+	140363184670464 [label="Class: 7"]
+	140363184670336 -> 140363184670464
+	140363184670528 [label="Class: 6"]
+	140363184670336 -> 140363184670528
+	140363184670592 [label="Class: 7"]
+	140363184669696 -> 140363184670592
+	140363184670656 [label="Is feature residual sugar(f_id: 3) <= 1.4?
+residual sugar
+Treshold: 1.4"]
+	140363184570176 -> 140363184670656
+	140363184670784 [label="Class: 6"]
+	140363184670656 -> 140363184670784
+	140363184670848 [label="Class: 7"]
+	140363184670656 -> 140363184670848
+	140363184670912 [label="Class: 6"]
+	140363184570112 -> 140363184670912
+	140363184670976 [label="Is feature sulphates(f_id: 9) <= 0.44?
+sulphates
+Treshold: 0.44"]
+	140363184565696 -> 140363184670976
+	140363184671104 [label="Is feature sulphates(f_id: 9) <= 0.42?
+sulphates
+Treshold: 0.42"]
+	140363184670976 -> 140363184671104
+	140363184671232 [label="Is feature alcohol(f_id: 10) <= 11.4?
+alcohol
+Treshold: 11.4"]
+	140363184671104 -> 140363184671232
+	140363184671360 [label="Class: 6"]
+	140363184671232 -> 140363184671360
+	140363184671424 [label="Class: 8"]
+	140363184671232 -> 140363184671424
+	140363184671488 [label="Class: 5"]
+	140363184671104 -> 140363184671488
+	140363184671552 [label="Class: 7"]
+	140363184670976 -> 140363184671552
+	140363184671616 [label="Is feature total sulfur dioxide(f_id: 6) <= 94.0?
+total sulfur dioxide
+Treshold: 94.0"]
+	140363184563520 -> 140363184671616
+	140363184671680 [label="Is feature pH(f_id: 8) <= 3.14?
+pH
+Treshold: 3.14"]
+	140363184671616 -> 140363184671680
+	140363184671808 [label="Is feature density(f_id: 7) <= 0.9914?
+density
+Treshold: 0.9914"]
+	140363184671680 -> 140363184671808
+	140363184671936 [label="Class: 6"]
+	140363184671808 -> 140363184671936
+	140363184672000 [label="Class: 8"]
+	140363184671808 -> 140363184672000
+	140363184672064 [label="Is feature citric acid(f_id: 2) <= 0.3?
+citric acid
+Treshold: 0.3"]
+	140363184671680 -> 140363184672064
+	140363184672192 [label="Is feature pH(f_id: 8) <= 3.18?
+pH
+Treshold: 3.18"]
+	140363184672064 -> 140363184672192
+	140363184672320 [label="Is feature residual sugar(f_id: 3) <= 1.1?
+residual sugar
+Treshold: 1.1"]
+	140363184672192 -> 140363184672320
+	140363184672448 [label="Class: 6"]
+	140363184672320 -> 140363184672448
+	140363184672512 [label="Class: 7"]
+	140363184672320 -> 140363184672512
+	140363184672576 [label="Class: 6"]
+	140363184672192 -> 140363184672576
+	140363184672640 [label="Is feature pH(f_id: 8) <= 3.23?
+pH
+Treshold: 3.23"]
+	140363184672064 -> 140363184672640
+	140363184672768 [label="Is feature chlorides(f_id: 4) <= 0.036?
+chlorides
+Treshold: 0.036"]
+	140363184672640 -> 140363184672768
+	140363184672896 [label="Class: 6"]
+	140363184672768 -> 140363184672896
+	140363184672960 [label="Class: 7"]
+	140363184672768 -> 140363184672960
+	140363184673024 [label="Is feature citric acid(f_id: 2) <= 0.39?
+citric acid
+Treshold: 0.39"]
+	140363184672640 -> 140363184673024
+	140363184673152 [label="Class: 7"]
+	140363184673024 -> 140363184673152
+	140363184673216 [label="Class: 6"]
+	140363184673024 -> 140363184673216
+	140363184673280 [label="Is feature volatile acidity(f_id: 1) <= 0.18?
+volatile acidity
+Treshold: 0.18"]
+	140363184671616 -> 140363184673280
+	140363184673344 [label="Is feature pH(f_id: 8) <= 3.31?
+pH
+Treshold: 3.31"]
+	140363184673280 -> 140363184673344
+	140363184673472 [label="Is feature citric acid(f_id: 2) <= 0.29?
+citric acid
+Treshold: 0.29"]
+	140363184673344 -> 140363184673472
+	140363184673600 [label="Class: 7"]
+	140363184673472 -> 140363184673600
+	140363184673664 [label="Is feature sulphates(f_id: 9) <= 0.68?
+sulphates
+Treshold: 0.68"]
+	140363184673472 -> 140363184673664
+	140363184673792 [label="Is feature alcohol(f_id: 10) <= 11.3?
+alcohol
+Treshold: 11.3"]
+	140363184673664 -> 140363184673792
+	140363184673920 [label="Class: 6"]
+	140363184673792 -> 140363184673920
+	140363184673984 [label="Class: 7"]
+	140363184673792 -> 140363184673984
+	140363184674048 [label="Class: 7"]
+	140363184673664 -> 140363184674048
+	140363184674112 [label="Is feature citric acid(f_id: 2) <= 0.33?
+citric acid
+Treshold: 0.33"]
+	140363184673344 -> 140363184674112
+	140363184674240 [label="Class: 7"]
+	140363184674112 -> 140363184674240
+	140363184674304 [label="Is feature free sulfur dioxide(f_id: 5) <= 29.0?
+free sulfur dioxide
+Treshold: 29.0"]
+	140363184674112 -> 140363184674304
+	140363184674368 [label="Class: 7"]
+	140363184674304 -> 140363184674368
+	140363184674432 [label="Class: 8"]
+	140363184674304 -> 140363184674432
+	140363184674496 [label="Is feature sulphates(f_id: 9) <= 0.34?
+sulphates
+Treshold: 0.34"]
+	140363184673280 -> 140363184674496
+	140363184674624 [label="Is feature residual sugar(f_id: 3) <= 3.5?
+residual sugar
+Treshold: 3.5"]
+	140363184674496 -> 140363184674624
+	140363184674752 [label="Is feature density(f_id: 7) <= 0.99024?
+density
+Treshold: 0.99024"]
+	140363184674624 -> 140363184674752
+	140363184674880 [label="Class: 7"]
+	140363184674752 -> 140363184674880
+	140363184674944 [label="Class: 5"]
+	140363184674752 -> 140363184674944
+	140363184675008 [label="Class: 8"]
+	140363184674624 -> 140363184675008
+	140363184675072 [label="Is feature volatile acidity(f_id: 1) <= 0.21?
+volatile acidity
+Treshold: 0.21"]
+	140363184674496 -> 140363184675072
+	140363184675136 [label="Is feature chlorides(f_id: 4) <= 0.047?
+chlorides
+Treshold: 0.047"]
+	140363184675072 -> 140363184675136
+	140363184675264 [label="Is feature chlorides(f_id: 4) <= 0.025?
+chlorides
+Treshold: 0.025"]
+	140363184675136 -> 140363184675264
+	140363184675392 [label="Class: 5"]
+	140363184675264 -> 140363184675392
+	140363184675456 [label="Class: 6"]
+	140363184675264 -> 140363184675456
+	140363184675520 [label="Class: 7"]
+	140363184675136 -> 140363184675520
+	140363184675584 [label="Is feature pH(f_id: 8) <= 3.1?
+pH
+Treshold: 3.1"]
+	140363184675072 -> 140363184675584
+	140363184675712 [label="Class: 6"]
+	140363184675584 -> 140363184675712
+	140363184675776 [label="Is feature free sulfur dioxide(f_id: 5) <= 26.0?
+free sulfur dioxide
+Treshold: 26.0"]
+	140363184675584 -> 140363184675776
+	140363184675840 [label="Is feature density(f_id: 7) <= 0.9906?
+density
+Treshold: 0.9906"]
+	140363184675776 -> 140363184675840
+	140363184675968 [label="Class: 5"]
+	140363184675840 -> 140363184675968
+	140363184676032 [label="Is feature density(f_id: 7) <= 0.9926?
+density
+Treshold: 0.9926"]
+	140363184675840 -> 140363184676032
+	140363184676160 [label="Is feature sulphates(f_id: 9) <= 0.46?
+sulphates
+Treshold: 0.46"]
+	140363184676032 -> 140363184676160
+	140363184676288 [label="Class: 7"]
+	140363184676160 -> 140363184676288
+	140363184676352 [label="Is feature fixed acidity(f_id: 0) <= 6.6?
+fixed acidity
+Treshold: 6.6"]
+	140363184676160 -> 140363184676352
+	140363184676480 [label="Class: 6"]
+	140363184676352 -> 140363184676480
+	140363184676544 [label="Class: 7"]
+	140363184676352 -> 140363184676544
+	140363184676608 [label="Class: 6"]
+	140363184676032 -> 140363184676608
+	140363184676672 [label="Is feature free sulfur dioxide(f_id: 5) <= 48.0?
+free sulfur dioxide
+Treshold: 48.0"]
+	140363184675776 -> 140363184676672
+	140363184676736 [label="Is feature chlorides(f_id: 4) <= 0.048?
+chlorides
+Treshold: 0.048"]
+	140363184676672 -> 140363184676736
+	140363184676864 [label="Is feature pH(f_id: 8) <= 3.38?
+pH
+Treshold: 3.38"]
+	140363184676736 -> 140363184676864
+	140363184676992 [label="Is feature free sulfur dioxide(f_id: 5) <= 31.0?
+free sulfur dioxide
+Treshold: 31.0"]
+	140363184676864 -> 140363184676992
+	140363184677056 [label="Is feature chlorides(f_id: 4) <= 0.045?
+chlorides
+Treshold: 0.045"]
+	140363184676992 -> 140363184677056
+	140363184677184 [label="Class: 5"]
+	140363184677056 -> 140363184677184
+	140363184677248 [label="Class: 7"]
+	140363184677056 -> 140363184677248
+	140363184677312 [label="Is feature total sulfur dioxide(f_id: 6) <= 116.0?
+total sulfur dioxide
+Treshold: 116.0"]
+	140363184676992 -> 140363184677312
+	140363184677376 [label="Class: 7"]
+	140363184677312 -> 140363184677376
+	140363184677440 [label="Is feature alcohol(f_id: 10) <= 11.3?
+alcohol
+Treshold: 11.3"]
+	140363184677312 -> 140363184677440
+	140363184677568 [label="Class: 7"]
+	140363184677440 -> 140363184677568
+	140363184677632 [label="Is feature sulphates(f_id: 9) <= 0.46?
+sulphates
+Treshold: 0.46"]
+	140363184677440 -> 140363184677632
+	140363184677760 [label="Class: 5"]
+	140363184677632 -> 140363184677760
+	140363184677824 [label="Class: 7"]
+	140363184677632 -> 140363184677824
+	140363184677888 [label="Class: 6"]
+	140363184676864 -> 140363184677888
+	140363184677952 [label="Class: 5"]
+	140363184676736 -> 140363184677952
+	140363184678016 [label="Class: 6"]
+	140363184676672 -> 140363184678016
+	140363184678080 [label="Is feature residual sugar(f_id: 3) <= 2.0?
+residual sugar
+Treshold: 2.0"]
+	140363184561280 -> 140363184678080
+	140363184678208 [label="Is feature total sulfur dioxide(f_id: 6) <= 124.0?
+total sulfur dioxide
+Treshold: 124.0"]
+	140363184678080 -> 140363184678208
+	140363184678272 [label="Is feature sulphates(f_id: 9) <= 0.37?
+sulphates
+Treshold: 0.37"]
+	140363184678208 -> 140363184678272
+	140363184678400 [label="Is feature sulphates(f_id: 9) <= 0.33?
+sulphates
+Treshold: 0.33"]
+	140363184678272 -> 140363184678400
+	140363184678528 [label="Is feature volatile acidity(f_id: 1) <= 0.36?
+volatile acidity
+Treshold: 0.36"]
+	140363184678400 -> 140363184678528
+	140363184678592 [label="Is feature residual sugar(f_id: 3) <= 1.6?
+residual sugar
+Treshold: 1.6"]
+	140363184678528 -> 140363184678592
+	140363184678720 [label="Class: 6"]
+	140363184678592 -> 140363184678720
+	140363184678784 [label="Class: 5"]
+	140363184678592 -> 140363184678784
+	140363184678848 [label="Class: 8"]
+	140363184678528 -> 140363184678848
+	140363184678912 [label="Is feature citric acid(f_id: 2) <= 0.29?
+citric acid
+Treshold: 0.29"]
+	140363184678400 -> 140363184678912
+	140363184679040 [label="Is feature free sulfur dioxide(f_id: 5) <= 19.0?
+free sulfur dioxide
+Treshold: 19.0"]
+	140363184678912 -> 140363184679040
+	140363184679104 [label="Class: 4"]
+	140363184679040 -> 140363184679104
+	140363184679168 [label="Class: 6"]
+	140363184679040 -> 140363184679168
+	140363184679232 [label="Is feature total sulfur dioxide(f_id: 6) <= 104.0?
+total sulfur dioxide
+Treshold: 104.0"]
+	140363184678912 -> 140363184679232
+	140363184679296 [label="Class: 7"]
+	140363184679232 -> 140363184679296
+	140363184679360 [label="Class: 4"]
+	140363184679232 -> 140363184679360
+	140363184679424 [label="Is feature volatile acidity(f_id: 1) <= 0.26?
+volatile acidity
+Treshold: 0.26"]
+	140363184678272 -> 140363184679424
+	140363184679488 [label="Is feature total sulfur dioxide(f_id: 6) <= 117.0?
+total sulfur dioxide
+Treshold: 117.0"]
+	140363184679424 -> 140363184679488
+	140363184679552 [label="Is feature chlorides(f_id: 4) <= 0.045?
+chlorides
+Treshold: 0.045"]
+	140363184679488 -> 140363184679552
+	140363184679680 [label="Is feature total sulfur dioxide(f_id: 6) <= 98.0?
+total sulfur dioxide
+Treshold: 98.0"]
+	140363184679552 -> 140363184679680
+	140363184679744 [label="Is feature free sulfur dioxide(f_id: 5) <= 15.0?
+free sulfur dioxide
+Treshold: 15.0"]
+	140363184679680 -> 140363184679744
+	140363184679808 [label="Is feature residual sugar(f_id: 3) <= 1.1?
+residual sugar
+Treshold: 1.1"]
+	140363184679744 -> 140363184679808
+	140363184679936 [label="Class: 8"]
+	140363184679808 -> 140363184679936
+	140363184680000 [label="Class: 7"]
+	140363184679808 -> 140363184680000
+	140363184680064 [label="Is feature residual sugar(f_id: 3) <= 0.8?
+residual sugar
+Treshold: 0.8"]
+	140363184679744 -> 140363184680064
+	140363184680192 [label="Class: 5"]
+	140363184680064 -> 140363184680192
+	140363184680256 [label="Is feature density(f_id: 7) <= 0.9918?
+density
+Treshold: 0.9918"]
+	140363184680064 -> 140363184680256
+	140363184680384 [label="Is feature density(f_id: 7) <= 0.98961?
+density
+Treshold: 0.98961"]
+	140363184680256 -> 140363184680384
+	140363184680512 [label="Is feature volatile acidity(f_id: 1) <= 0.16?
+volatile acidity
+Treshold: 0.16"]
+	140363184680384 -> 140363184680512
+	140363184680576 [label="Class: 6"]
+	140363184680512 -> 140363184680576
+	140363184680640 [label="Class: 7"]
+	140363184680512 -> 140363184680640
+	140363184680704 [label="Class: 6"]
+	140363184680384 -> 140363184680704
+	140363184680768 [label="Class: 7"]
+	140363184680256 -> 140363184680768
+	140363184680832 [label="Is feature sulphates(f_id: 9) <= 0.54?
+sulphates
+Treshold: 0.54"]
+	140363184679680 -> 140363184680832
+	140363184680960 [label="Is feature total sulfur dioxide(f_id: 6) <= 111.0?
+total sulfur dioxide
+Treshold: 111.0"]
+	140363184680832 -> 140363184680960
+	140363184681024 [label="Is feature residual sugar(f_id: 3) <= 1.3?
+residual sugar
+Treshold: 1.3"]
+	140363184680960 -> 140363184681024
+	140363184681152 [label="Is feature free sulfur dioxide(f_id: 5) <= 22.0?
+free sulfur dioxide
+Treshold: 22.0"]
+	140363184681024 -> 140363184681152
+	140363184681216 [label="Class: 6"]
+	140363184681152 -> 140363184681216
+	140363184681280 [label="Is feature alcohol(f_id: 10) <= 11.4?
+alcohol
+Treshold: 11.4"]
+	140363184681152 -> 140363184681280
+	140363184681408 [label="Class: 5"]
+	140363184681280 -> 140363184681408
+	140363184681472 [label="Class: 6"]
+	140363184681280 -> 140363184681472
+	140363184681536 [label="Class: 6"]
+	140363184681024 -> 140363184681536
+	140363184681600 [label="Class: 7"]
+	140363184680960 -> 140363184681600
+	140363184681664 [label="Is feature alcohol(f_id: 10) <= 11.3?
+alcohol
+Treshold: 11.3"]
+	140363184680832 -> 140363184681664
+	140363184681792 [label="Is feature pH(f_id: 8) <= 3.05?
+pH
+Treshold: 3.05"]
+	140363184681664 -> 140363184681792
+	140363184681920 [label="Class: 5"]
+	140363184681792 -> 140363184681920
+	140363184681984 [label="Class: 7"]
+	140363184681792 -> 140363184681984
+	140363184682048 [label="Class: 5"]
+	140363184681664 -> 140363184682048
+	140363184682112 [label="Class: 6"]
+	140363184679552 -> 140363184682112
+	140363184682176 [label="Is feature volatile acidity(f_id: 1) <= 0.2?
+volatile acidity
+Treshold: 0.2"]
+	140363184679488 -> 140363184682176
+	140363184682240 [label="Class: 6"]
+	140363184682176 -> 140363184682240
+	140363184682304 [label="Is feature sulphates(f_id: 9) <= 0.5?
+sulphates
+Treshold: 0.5"]
+	140363184682176 -> 140363184682304
+	140363184682432 [label="Class: 8"]
+	140363184682304 -> 140363184682432
+	140363184682496 [label="Class: 7"]
+	140363184682304 -> 140363184682496
+	140363184682560 [label="Is feature volatile acidity(f_id: 1) <= 0.29?
+volatile acidity
+Treshold: 0.29"]
+	140363184679424 -> 140363184682560
+	140363184682624 [label="Is feature alcohol(f_id: 10) <= 11.4?
+alcohol
+Treshold: 11.4"]
+	140363184682560 -> 140363184682624
+	140363184682752 [label="Class: 5"]
+	140363184682624 -> 140363184682752
+	140363184682816 [label="Is feature chlorides(f_id: 4) <= 0.039?
+chlorides
+Treshold: 0.039"]
+	140363184682624 -> 140363184682816
+	140363184682944 [label="Is feature density(f_id: 7) <= 0.99119?
+density
+Treshold: 0.99119"]
+	140363184682816 -> 140363184682944
+	140363184683072 [label="Class: 6"]
+	140363184682944 -> 140363184683072
+	140363184683136 [label="Class: 5"]
+	140363184682944 -> 140363184683136
+	140363184683200 [label="Class: 5"]
+	140363184682816 -> 140363184683200
+	140363184683264 [label="Is feature pH(f_id: 8) <= 3.15?
+pH
+Treshold: 3.15"]
+	140363184682560 -> 140363184683264
+	140363184683392 [label="Class: 6"]
+	140363184683264 -> 140363184683392
+	140363184683456 [label="Is feature residual sugar(f_id: 3) <= 1.6?
+residual sugar
+Treshold: 1.6"]
+	140363184683264 -> 140363184683456
+	140363184683584 [label="Class: 7"]
+	140363184683456 -> 140363184683584
+	140363184683648 [label="Class: 6"]
+	140363184683456 -> 140363184683648
+	140363184683712 [label="Is feature pH(f_id: 8) <= 2.96?
+pH
+Treshold: 2.96"]
+	140363184678208 -> 140363184683712
+	140363184683840 [label="Class: 5"]
+	140363184683712 -> 140363184683840
+	140363184683904 [label="Is feature free sulfur dioxide(f_id: 5) <= 37.0?
+free sulfur dioxide
+Treshold: 37.0"]
+	140363184683712 -> 140363184683904
+	140363184683968 [label="Is feature residual sugar(f_id: 3) <= 1.8?
+residual sugar
+Treshold: 1.8"]
+	140363184683904 -> 140363184683968
+	140363184684096 [label="Is feature density(f_id: 7) <= 0.9917?
+density
+Treshold: 0.9917"]
+	140363184683968 -> 140363184684096
+	140363184684224 [label="Is feature citric acid(f_id: 2) <= 0.27?
+citric acid
+Treshold: 0.27"]
+	140363184684096 -> 140363184684224
+	140363184684352 [label="Class: 5"]
+	140363184684224 -> 140363184684352
+	140363184684416 [label="Is feature free sulfur dioxide(f_id: 5) <= 34.0?
+free sulfur dioxide
+Treshold: 34.0"]
+	140363184684224 -> 140363184684416
+	140363184684480 [label="Class: 6"]
+	140363184684416 -> 140363184684480
+	140363184684544 [label="Is feature residual sugar(f_id: 3) <= 1.1?
+residual sugar
+Treshold: 1.1"]
+	140363184684416 -> 140363184684544
+	140363184684672 [label="Class: 5"]
+	140363184684544 -> 140363184684672
+	140363184684736 [label="Is feature alcohol(f_id: 10) <= 11.2?
+alcohol
+Treshold: 11.2"]
+	140363184684544 -> 140363184684736
+	140363184684864 [label="Class: 5"]
+	140363184684736 -> 140363184684864
+	140363184684928 [label="Class: 6"]
+	140363184684736 -> 140363184684928
+	140363184684992 [label="Is feature citric acid(f_id: 2) <= 0.29?
+citric acid
+Treshold: 0.29"]
+	140363184684096 -> 140363184684992
+	140363184750720 [label="Is feature volatile acidity(f_id: 1) <= 0.19?
+volatile acidity
+Treshold: 0.19"]
+	140363184684992 -> 140363184750720
+	140363184750784 [label="Class: 7"]
+	140363184750720 -> 140363184750784
+	140363184750848 [label="Class: 6"]
+	140363184750720 -> 140363184750848
+	140363184750912 [label="Is feature pH(f_id: 8) <= 3.11?
+pH
+Treshold: 3.11"]
+	140363184684992 -> 140363184750912
+	140363184751040 [label="Class: 7"]
+	140363184750912 -> 140363184751040
+	140363184751104 [label="Is feature free sulfur dioxide(f_id: 5) <= 27.0?
+free sulfur dioxide
+Treshold: 27.0"]
+	140363184750912 -> 140363184751104
+	140363184751168 [label="Class: 5"]
+	140363184751104 -> 140363184751168
+	140363184751232 [label="Is feature volatile acidity(f_id: 1) <= 0.18?
+volatile acidity
+Treshold: 0.18"]
+	140363184751104 -> 140363184751232
+	140363184751296 [label="Class: 5"]
+	140363184751232 -> 140363184751296
+	140363184751360 [label="Class: 6"]
+	140363184751232 -> 140363184751360
+	140363184751424 [label="Class: 5"]
+	140363184683968 -> 140363184751424
+	140363184751488 [label="Is feature total sulfur dioxide(f_id: 6) <= 150.0?
+total sulfur dioxide
+Treshold: 150.0"]
+	140363184683904 -> 140363184751488
+	140363184751552 [label="Is feature alcohol(f_id: 10) <= 11.2?
+alcohol
+Treshold: 11.2"]
+	140363184751488 -> 140363184751552
+	140363184751680 [label="Is feature free sulfur dioxide(f_id: 5) <= 42.0?
+free sulfur dioxide
+Treshold: 42.0"]
+	140363184751552 -> 140363184751680
+	140363184751744 [label="Class: 6"]
+	140363184751680 -> 140363184751744
+	140363184751808 [label="Class: 7"]
+	140363184751680 -> 140363184751808
+	140363184751872 [label="Class: 7"]
+	140363184751552 -> 140363184751872
+	140363184751936 [label="Is feature citric acid(f_id: 2) <= 0.37?
+citric acid
+Treshold: 0.37"]
+	140363184751488 -> 140363184751936
+	140363184752064 [label="Is feature volatile acidity(f_id: 1) <= 0.4?
+volatile acidity
+Treshold: 0.4"]
+	140363184751936 -> 140363184752064
+	140363184752128 [label="Is feature fixed acidity(f_id: 0) <= 7.1?
+fixed acidity
+Treshold: 7.1"]
+	140363184752064 -> 140363184752128
+	140363184752256 [label="Is feature density(f_id: 7) <= 0.99174?
+density
+Treshold: 0.99174"]
+	140363184752128 -> 140363184752256
+	140363184752384 [label="Class: 6"]
+	140363184752256 -> 140363184752384
+	140363184752448 [label="Class: 7"]
+	140363184752256 -> 140363184752448
+	140363184752512 [label="Class: 6"]
+	140363184752128 -> 140363184752512
+	140363184752576 [label="Class: 5"]
+	140363184752064 -> 140363184752576
+	140363184752640 [label="Is feature volatile acidity(f_id: 1) <= 0.16?
+volatile acidity
+Treshold: 0.16"]
+	140363184751936 -> 140363184752640
+	140363184752704 [label="Class: 7"]
+	140363184752640 -> 140363184752704
+	140363184752768 [label="Class: 5"]
+	140363184752640 -> 140363184752768
+	140363184752832 [label="Is feature total sulfur dioxide(f_id: 6) <= 76.0?
+total sulfur dioxide
+Treshold: 76.0"]
+	140363184678080 -> 140363184752832
+	140363184752896 [label="Is feature residual sugar(f_id: 3) <= 6.6?
+residual sugar
+Treshold: 6.6"]
+	140363184752832 -> 140363184752896
+	140363184753024 [label="Is feature chlorides(f_id: 4) <= 0.038?
+chlorides
+Treshold: 0.038"]
+	140363184752896 -> 140363184753024
+	140363184753152 [label="Class: 7"]
+	140363184753024 -> 140363184753152
+	140363184753216 [label="Class: 4"]
+	140363184753024 -> 140363184753216
+	140363184753280 [label="Is feature density(f_id: 7) <= 0.9956?
+density
+Treshold: 0.9956"]
+	140363184752896 -> 140363184753280
+	140363184753408 [label="Is feature pH(f_id: 8) <= 3.17?
+pH
+Treshold: 3.17"]
+	140363184753280 -> 140363184753408
+	140363184753536 [label="Class: 5"]
+	140363184753408 -> 140363184753536
+	140363184753600 [label="Class: 3"]
+	140363184753408 -> 140363184753600
+	140363184753664 [label="Class: 6"]
+	140363184753280 -> 140363184753664
+	140363184753728 [label="Is feature pH(f_id: 8) <= 3.24?
+pH
+Treshold: 3.24"]
+	140363184752832 -> 140363184753728
+	140363184753856 [label="Is feature fixed acidity(f_id: 0) <= 7.2?
+fixed acidity
+Treshold: 7.2"]
+	140363184753728 -> 140363184753856
+	140363184753984 [label="Is feature total sulfur dioxide(f_id: 6) <= 157.0?
+total sulfur dioxide
+Treshold: 157.0"]
+	140363184753856 -> 140363184753984
+	140363184754048 [label="Is feature total sulfur dioxide(f_id: 6) <= 112.0?
+total sulfur dioxide
+Treshold: 112.0"]
+	140363184753984 -> 140363184754048
+	140363184754112 [label="Is feature volatile acidity(f_id: 1) <= 0.21?
+volatile acidity
+Treshold: 0.21"]
+	140363184754048 -> 140363184754112
+	140363184754176 [label="Is feature chlorides(f_id: 4) <= 0.034?
+chlorides
+Treshold: 0.034"]
+	140363184754112 -> 140363184754176
+	140363184754304 [label="Is feature sulphates(f_id: 9) <= 0.52?
+sulphates
+Treshold: 0.52"]
+	140363184754176 -> 140363184754304
+	140363184754432 [label="Class: 7"]
+	140363184754304 -> 140363184754432
+	140363184754496 [label="Is feature residual sugar(f_id: 3) <= 2.3?
+residual sugar
+Treshold: 2.3"]
+	140363184754304 -> 140363184754496
+	140363184754624 [label="Class: 7"]
+	140363184754496 -> 140363184754624
+	140363184754688 [label="Class: 6"]
+	140363184754496 -> 140363184754688
+	140363184754752 [label="Class: 6"]
+	140363184754176 -> 140363184754752
+	140363184754816 [label="Is feature total sulfur dioxide(f_id: 6) <= 104.0?
+total sulfur dioxide
+Treshold: 104.0"]
+	140363184754112 -> 140363184754816
+	140363184754880 [label="Class: 6"]
+	140363184754816 -> 140363184754880
+	140363184754944 [label="Is feature residual sugar(f_id: 3) <= 3.5?
+residual sugar
+Treshold: 3.5"]
+	140363184754816 -> 140363184754944
+	140363184755072 [label="Class: 6"]
+	140363184754944 -> 140363184755072
+	140363184755136 [label="Class: 5"]
+	140363184754944 -> 140363184755136
+	140363184755200 [label="Is feature residual sugar(f_id: 3) <= 6.3?
+residual sugar
+Treshold: 6.3"]
+	140363184754048 -> 140363184755200
+	140363184755328 [label="Is feature free sulfur dioxide(f_id: 5) <= 39.0?
+free sulfur dioxide
+Treshold: 39.0"]
+	140363184755200 -> 140363184755328
+	140363184755392 [label="Is feature total sulfur dioxide(f_id: 6) <= 156.0?
+total sulfur dioxide
+Treshold: 156.0"]
+	140363184755328 -> 140363184755392
+	140363184755456 [label="Class: 6"]
+	140363184755392 -> 140363184755456
+	140363184755520 [label="Class: 7"]
+	140363184755392 -> 140363184755520
+	140363184755584 [label="Is feature volatile acidity(f_id: 1) <= 0.19?
+volatile acidity
+Treshold: 0.19"]
+	140363184755328 -> 140363184755584
+	140363184755648 [label="Is feature residual sugar(f_id: 3) <= 4.9?
+residual sugar
+Treshold: 4.9"]
+	140363184755584 -> 140363184755648
+	140363184755776 [label="Class: 6"]
+	140363184755648 -> 140363184755776
+	140363184755840 [label="Class: 7"]
+	140363184755648 -> 140363184755840
+	140363184755904 [label="Class: 7"]
+	140363184755584 -> 140363184755904
+	140363184755968 [label="Is feature alcohol(f_id: 10) <= 11.5?
+alcohol
+Treshold: 11.5"]
+	140363184755200 -> 140363184755968
+	140363184756096 [label="Is feature chlorides(f_id: 4) <= 0.037?
+chlorides
+Treshold: 0.037"]
+	140363184755968 -> 140363184756096
+	140363184756224 [label="Is feature fixed acidity(f_id: 0) <= 7.1?
+fixed acidity
+Treshold: 7.1"]
+	140363184756096 -> 140363184756224
+	140363184756352 [label="Class: 6"]
+	140363184756224 -> 140363184756352
+	140363184756416 [label="Class: 8"]
+	140363184756224 -> 140363184756416
+	140363184756480 [label="Is feature total sulfur dioxide(f_id: 6) <= 128.0?
+total sulfur dioxide
+Treshold: 128.0"]
+	140363184756096 -> 140363184756480
+	140363184756544 [label="Is feature density(f_id: 7) <= 0.99326?
+density
+Treshold: 0.99326"]
+	140363184756480 -> 140363184756544
+	140363184756672 [label="Is feature free sulfur dioxide(f_id: 5) <= 43.0?
+free sulfur dioxide
+Treshold: 43.0"]
+	140363184756544 -> 140363184756672
+	140363184756736 [label="Class: 8"]
+	140363184756672 -> 140363184756736
+	140363184756800 [label="Class: 7"]
+	140363184756672 -> 140363184756800
+	140363184756864 [label="Class: 7"]
+	140363184756544 -> 140363184756864
+	140363184756928 [label="Class: 6"]
+	140363184756480 -> 140363184756928
+	140363184756992 [label="Is feature free sulfur dioxide(f_id: 5) <= 37.0?
+free sulfur dioxide
+Treshold: 37.0"]
+	140363184755968 -> 140363184756992
+	140363184757056 [label="Class: 8"]
+	140363184756992 -> 140363184757056
+	140363184757120 [label="Class: 7"]
+	140363184756992 -> 140363184757120
+	140363184757184 [label="Is feature residual sugar(f_id: 3) <= 2.2?
+residual sugar
+Treshold: 2.2"]
+	140363184753984 -> 140363184757184
+	140363184757312 [label="Class: 5"]
+	140363184757184 -> 140363184757312
+	140363184757376 [label="Is feature density(f_id: 7) <= 0.99382?
+density
+Treshold: 0.99382"]
+	140363184757184 -> 140363184757376
+	140363184757504 [label="Class: 6"]
+	140363184757376 -> 140363184757504
+	140363184757568 [label="Is feature pH(f_id: 8) <= 3.13?
+pH
+Treshold: 3.13"]
+	140363184757376 -> 140363184757568
+	140363184757696 [label="Class: 6"]
+	140363184757568 -> 140363184757696
+	140363184757760 [label="Class: 5"]
+	140363184757568 -> 140363184757760
+	140363184757824 [label="Is feature sulphates(f_id: 9) <= 0.51?
+sulphates
+Treshold: 0.51"]
+	140363184753856 -> 140363184757824
+	140363184757952 [label="Is feature chlorides(f_id: 4) <= 0.039?
+chlorides
+Treshold: 0.039"]
+	140363184757824 -> 140363184757952
+	140363184758080 [label="Is feature sulphates(f_id: 9) <= 0.42?
+sulphates
+Treshold: 0.42"]
+	140363184757952 -> 140363184758080
+	140363184758208 [label="Is feature residual sugar(f_id: 3) <= 7.6?
+residual sugar
+Treshold: 7.6"]
+	140363184758080 -> 140363184758208
+	140363184758336 [label="Is feature free sulfur dioxide(f_id: 5) <= 46.0?
+free sulfur dioxide
+Treshold: 46.0"]
+	140363184758208 -> 140363184758336
+	140363184758400 [label="Is feature density(f_id: 7) <= 0.9924?
+density
+Treshold: 0.9924"]
+	140363184758336 -> 140363184758400
+	140363184758528 [label="Is feature free sulfur dioxide(f_id: 5) <= 29.0?
+free sulfur dioxide
+Treshold: 29.0"]
+	140363184758400 -> 140363184758528
+	140363184758592 [label="Class: 7"]
+	140363184758528 -> 140363184758592
+	140363184758656 [label="Is feature residual sugar(f_id: 3) <= 5.0?
+residual sugar
+Treshold: 5.0"]
+	140363184758528 -> 140363184758656
+	140363184758784 [label="Class: 6"]
+	140363184758656 -> 140363184758784
+	140363184758848 [label="Class: 7"]
+	140363184758656 -> 140363184758848
+	140363184758912 [label="Is feature fixed acidity(f_id: 0) <= 8.4?
+fixed acidity
+Treshold: 8.4"]
+	140363184758400 -> 140363184758912
+	140363184759040 [label="Is feature chlorides(f_id: 4) <= 0.036?
+chlorides
+Treshold: 0.036"]
+	140363184758912 -> 140363184759040
+	140363184759168 [label="Is feature sulphates(f_id: 9) <= 0.37?
+sulphates
+Treshold: 0.37"]
+	140363184759040 -> 140363184759168
+	140363184759296 [label="Class: 5"]
+	140363184759168 -> 140363184759296
+	140363184759360 [label="Class: 7"]
+	140363184759168 -> 140363184759360
+	140363184759424 [label="Class: 7"]
+	140363184759040 -> 140363184759424
+	140363184759488 [label="Is feature volatile acidity(f_id: 1) <= 0.3?
+volatile acidity
+Treshold: 0.3"]
+	140363184758912 -> 140363184759488
+	140363184759552 [label="Class: 6"]
+	140363184759488 -> 140363184759552
+	140363184759616 [label="Class: 5"]
+	140363184759488 -> 140363184759616
+	140363184759680 [label="Class: 6"]
+	140363184758336 -> 140363184759680
+	140363184759744 [label="Is feature total sulfur dioxide(f_id: 6) <= 112.0?
+total sulfur dioxide
+Treshold: 112.0"]
+	140363184758208 -> 140363184759744
+	140363184759808 [label="Class: 6"]
+	140363184759744 -> 140363184759808
+	140363184759872 [label="Is feature fixed acidity(f_id: 0) <= 7.6?
+fixed acidity
+Treshold: 7.6"]
+	140363184759744 -> 140363184759872
+	140363184760000 [label="Class: 7"]
+	140363184759872 -> 140363184760000
+	140363184760064 [label="Is feature alcohol(f_id: 10) <= 11.2?
+alcohol
+Treshold: 11.2"]
+	140363184759872 -> 140363184760064
+	140363184760192 [label="Class: 7"]
+	140363184760064 -> 140363184760192
+	140363184760256 [label="Class: 6"]
+	140363184760064 -> 140363184760256
+	140363184760320 [label="Is feature total sulfur dioxide(f_id: 6) <= 120.0?
+total sulfur dioxide
+Treshold: 120.0"]
+	140363184758080 -> 140363184760320
+	140363184760384 [label="Class: 5"]
+	140363184760320 -> 140363184760384
+	140363184760448 [label="Is feature total sulfur dioxide(f_id: 6) <= 167.0?
+total sulfur dioxide
+Treshold: 167.0"]
+	140363184760320 -> 140363184760448
+	140363184760512 [label="Class: 7"]
+	140363184760448 -> 140363184760512
+	140363184760576 [label="Class: 5"]
+	140363184760448 -> 140363184760576
+	140363184760640 [label="Is feature pH(f_id: 8) <= 3.13?
+pH
+Treshold: 3.13"]
+	140363184757952 -> 140363184760640
+	140363184760768 [label="Is feature density(f_id: 7) <= 0.9955?
+density
+Treshold: 0.9955"]
+	140363184760640 -> 140363184760768
+	140363184760896 [label="Is feature volatile acidity(f_id: 1) <= 0.23?
+volatile acidity
+Treshold: 0.23"]
+	140363184760768 -> 140363184760896
+	140363184760960 [label="Is feature residual sugar(f_id: 3) <= 4.6?
+residual sugar
+Treshold: 4.6"]
+	140363184760896 -> 140363184760960
+	140363184761088 [label="Class: 6"]
+	140363184760960 -> 140363184761088
+	140363184761152 [label="Class: 5"]
+	140363184760960 -> 140363184761152
+	140363184761216 [label="Class: 6"]
+	140363184760896 -> 140363184761216
+	140363184761280 [label="Class: 7"]
+	140363184760768 -> 140363184761280
+	140363184761344 [label="Is feature sulphates(f_id: 9) <= 0.38?
+sulphates
+Treshold: 0.38"]
+	140363184760640 -> 140363184761344
+	140363184761472 [label="Is feature residual sugar(f_id: 3) <= 3.15?
+residual sugar
+Treshold: 3.15"]
+	140363184761344 -> 140363184761472
+	140363184761600 [label="Class: 5"]
+	140363184761472 -> 140363184761600
+	140363184761664 [label="Class: 6"]
+	140363184761472 -> 140363184761664
+	140363184761728 [label="Is feature sulphates(f_id: 9) <= 0.39?
+sulphates
+Treshold: 0.39"]
+	140363184761344 -> 140363184761728
+	140363184761856 [label="Class: 7"]
+	140363184761728 -> 140363184761856
+	140363184761920 [label="Class: 5"]
+	140363184761728 -> 140363184761920
+	140363184761984 [label="Is feature citric acid(f_id: 2) <= 0.33?
+citric acid
+Treshold: 0.33"]
+	140363184757824 -> 140363184761984
+	140363184762112 [label="Is feature fixed acidity(f_id: 0) <= 7.4?
+fixed acidity
+Treshold: 7.4"]
+	140363184761984 -> 140363184762112
+	140363184762240 [label="Is feature free sulfur dioxide(f_id: 5) <= 18.0?
+free sulfur dioxide
+Treshold: 18.0"]
+	140363184762112 -> 140363184762240
+	140363184762304 [label="Class: 4"]
+	140363184762240 -> 140363184762304
+	140363184762368 [label="Class: 7"]
+	140363184762240 -> 140363184762368
+	140363184762432 [label="Is feature density(f_id: 7) <= 0.9916?
+density
+Treshold: 0.9916"]
+	140363184762112 -> 140363184762432
+	140363184762560 [label="Is feature pH(f_id: 8) <= 2.99?
+pH
+Treshold: 2.99"]
+	140363184762432 -> 140363184762560
+	140363184762688 [label="Class: 6"]
+	140363184762560 -> 140363184762688
+	140363184762752 [label="Class: 7"]
+	140363184762560 -> 140363184762752
+	140363184762816 [label="Class: 6"]
+	140363184762432 -> 140363184762816
+	140363184762880 [label="Class: 6"]
+	140363184761984 -> 140363184762880
+	140363184762944 [label="Is feature pH(f_id: 8) <= 3.36?
+pH
+Treshold: 3.36"]
+	140363184753728 -> 140363184762944
+	140363184763072 [label="Is feature density(f_id: 7) <= 0.9924?
+density
+Treshold: 0.9924"]
+	140363184762944 -> 140363184763072
+	140363184763200 [label="Is feature free sulfur dioxide(f_id: 5) <= 35.0?
+free sulfur dioxide
+Treshold: 35.0"]
+	140363184763072 -> 140363184763200
+	140363184763264 [label="Class: 8"]
+	140363184763200 -> 140363184763264
+	140363184763328 [label="Class: 7"]
+	140363184763200 -> 140363184763328
+	140363184763392 [label="Is feature sulphates(f_id: 9) <= 0.37?
+sulphates
+Treshold: 0.37"]
+	140363184763072 -> 140363184763392
+	140363184763520 [label="Class: 8"]
+	140363184763392 -> 140363184763520
+	140363184763584 [label="Is feature total sulfur dioxide(f_id: 6) <= 128.0?
+total sulfur dioxide
+Treshold: 128.0"]
+	140363184763392 -> 140363184763584
+	140363184763648 [label="Is feature residual sugar(f_id: 3) <= 5.4?
+residual sugar
+Treshold: 5.4"]
+	140363184763584 -> 140363184763648
+	140363184763776 [label="Class: 8"]
+	140363184763648 -> 140363184763776
+	140363184763840 [label="Class: 6"]
+	140363184763648 -> 140363184763840
+	140363184763904 [label="Class: 6"]
+	140363184763584 -> 140363184763904
+	140363184763968 [label="Is feature volatile acidity(f_id: 1) <= 0.38?
+volatile acidity
+Treshold: 0.38"]
+	140363184762944 -> 140363184763968
+	140363184764032 [label="Class: 7"]
+	140363184763968 -> 140363184764032
+	140363184764096 [label="Class: 6"]
+	140363184763968 -> 140363184764096
+	140363184764160 [label="Is feature sulphates(f_id: 9) <= 0.38?
+sulphates
+Treshold: 0.38"]
+	140363184561216 -> 140363184764160
+	140363184764288 [label="Is feature free sulfur dioxide(f_id: 5) <= 75.0?
+free sulfur dioxide
+Treshold: 75.0"]
+	140363184764160 -> 140363184764288
+	140363184764352 [label="Class: 4"]
+	140363184764288 -> 140363184764352
+	140363184764416 [label="Class: 3"]
+	140363184764288 -> 140363184764416
+	140363184764480 [label="Is feature pH(f_id: 8) <= 3.38?
+pH
+Treshold: 3.38"]
+	140363184764160 -> 140363184764480
+	140363184764608 [label="Is feature pH(f_id: 8) <= 2.92?
+pH
+Treshold: 2.92"]
+	140363184764480 -> 140363184764608
+	140363184764736 [label="Class: 6"]
+	140363184764608 -> 140363184764736
+	140363184764800 [label="Class: 8"]
+	140363184764608 -> 140363184764800
+	140363184764864 [label="Is feature fixed acidity(f_id: 0) <= 4.2?
+fixed acidity
+Treshold: 4.2"]
+	140363184764480 -> 140363184764864
+	140363184764992 [label="Class: 7"]
+	140363184764864 -> 140363184764992
+	140363184765056 [label="Class: 6"]
+	140363184764864 -> 140363184765056
+	140363184765120 [label="Is feature citric acid(f_id: 2) <= 0.4?
+citric acid
+Treshold: 0.4"]
+	140363184561088 -> 140363184765120
+	140363184765248 [label="Is feature free sulfur dioxide(f_id: 5) <= 20.0?
+free sulfur dioxide
+Treshold: 20.0"]
+	140363184765120 -> 140363184765248
+	140363184765312 [label="Is feature density(f_id: 7) <= 0.98952?
+density
+Treshold: 0.98952"]
+	140363184765248 -> 140363184765312
+	140363184765440 [label="Is feature volatile acidity(f_id: 1) <= 0.36?
+volatile acidity
+Treshold: 0.36"]
+	140363184765312 -> 140363184765440
+	140363184765504 [label="Is feature sulphates(f_id: 9) <= 0.49?
+sulphates
+Treshold: 0.49"]
+	140363184765440 -> 140363184765504
+	140363184765632 [label="Is feature pH(f_id: 8) <= 3.51?
+pH
+Treshold: 3.51"]
+	140363184765504 -> 140363184765632
+	140363184765760 [label="Class: 7"]
+	140363184765632 -> 140363184765760
+	140363184765824 [label="Class: 8"]
+	140363184765632 -> 140363184765824
+	140363184765888 [label="Is feature volatile acidity(f_id: 1) <= 0.28?
+volatile acidity
+Treshold: 0.28"]
+	140363184765504 -> 140363184765888
+	140363184765952 [label="Class: 6"]
+	140363184765888 -> 140363184765952
+	140363184766016 [label="Class: 7"]
+	140363184765888 -> 140363184766016
+	140363184766080 [label="Is feature free sulfur dioxide(f_id: 5) <= 17.0?
+free sulfur dioxide
+Treshold: 17.0"]
+	140363184765440 -> 140363184766080
+	140363184766144 [label="Class: 6"]
+	140363184766080 -> 140363184766144
+	140363184766208 [label="Is feature citric acid(f_id: 2) <= 0.27?
+citric acid
+Treshold: 0.27"]
+	140363184766080 -> 140363184766208
+	140363184766336 [label="Class: 8"]
+	140363184766208 -> 140363184766336
+	140363184766400 [label="Class: 4"]
+	140363184766208 -> 140363184766400
+	140363184766464 [label="Is feature citric acid(f_id: 2) <= 0.24?
+citric acid
+Treshold: 0.24"]
+	140363184765312 -> 140363184766464
+	140363184766592 [label="Is feature pH(f_id: 8) <= 3.42?
+pH
+Treshold: 3.42"]
+	140363184766464 -> 140363184766592
+	140363184766720 [label="Is feature total sulfur dioxide(f_id: 6) <= 73.0?
+total sulfur dioxide
+Treshold: 73.0"]
+	140363184766592 -> 140363184766720
+	140363184766784 [label="Class: 5"]
+	140363184766720 -> 140363184766784
+	140363184766848 [label="Class: 7"]
+	140363184766720 -> 140363184766848
+	140363184766912 [label="Is feature volatile acidity(f_id: 1) <= 0.21?
+volatile acidity
+Treshold: 0.21"]
+	140363184766592 -> 140363184766912
+	140363184816192 [label="Class: 7"]
+	140363184766912 -> 140363184816192
+	140363184816256 [label="Class: 6"]
+	140363184766912 -> 140363184816256
+	140363184816320 [label="Is feature sulphates(f_id: 9) <= 0.33?
+sulphates
+Treshold: 0.33"]
+	140363184766464 -> 140363184816320
+	140363184816448 [label="Class: 7"]
+	140363184816320 -> 140363184816448
+	140363184816512 [label="Is feature volatile acidity(f_id: 1) <= 0.44?
+volatile acidity
+Treshold: 0.44"]
+	140363184816320 -> 140363184816512
+	140363184816576 [label="Is feature pH(f_id: 8) <= 3.32?
+pH
+Treshold: 3.32"]
+	140363184816512 -> 140363184816576
+	140363184816704 [label="Class: 6"]
+	140363184816576 -> 140363184816704
+	140363184816768 [label="Is feature residual sugar(f_id: 3) <= 3.3?
+residual sugar
+Treshold: 3.3"]
+	140363184816576 -> 140363184816768
+	140363184816896 [label="Is feature density(f_id: 7) <= 0.99008?
+density
+Treshold: 0.99008"]
+	140363184816768 -> 140363184816896
+	140363184817024 [label="Class: 6"]
+	140363184816896 -> 140363184817024
+	140363184817088 [label="Class: 7"]
+	140363184816896 -> 140363184817088
+	140363184817152 [label="Class: 6"]
+	140363184816768 -> 140363184817152
+	140363184817216 [label="Is feature sulphates(f_id: 9) <= 0.53?
+sulphates
+Treshold: 0.53"]
+	140363184816512 -> 140363184817216
+	140363184817344 [label="Is feature free sulfur dioxide(f_id: 5) <= 17.0?
+free sulfur dioxide
+Treshold: 17.0"]
+	140363184817216 -> 140363184817344
+	140363184817408 [label="Class: 7"]
+	140363184817344 -> 140363184817408
+	140363184817472 [label="Class: 5"]
+	140363184817344 -> 140363184817472
+	140363184817536 [label="Class: 6"]
+	140363184817216 -> 140363184817536
+	140363184817600 [label="Is feature sulphates(f_id: 9) <= 0.47?
+sulphates
+Treshold: 0.47"]
+	140363184765248 -> 140363184817600
+	140363184817728 [label="Is feature free sulfur dioxide(f_id: 5) <= 54.0?
+free sulfur dioxide
+Treshold: 54.0"]
+	140363184817600 -> 140363184817728
+	140363184817792 [label="Is feature total sulfur dioxide(f_id: 6) <= 122.0?
+total sulfur dioxide
+Treshold: 122.0"]
+	140363184817728 -> 140363184817792
+	140363184817856 [label="Is feature volatile acidity(f_id: 1) <= 0.27?
+volatile acidity
+Treshold: 0.27"]
+	140363184817792 -> 140363184817856
+	140363184817920 [label="Is feature total sulfur dioxide(f_id: 6) <= 117.0?
+total sulfur dioxide
+Treshold: 117.0"]
+	140363184817856 -> 140363184817920
+	140363184817984 [label="Is feature free sulfur dioxide(f_id: 5) <= 22.0?
+free sulfur dioxide
+Treshold: 22.0"]
+	140363184817920 -> 140363184817984
+	140363184818048 [label="Is feature residual sugar(f_id: 3) <= 2.9?
+residual sugar
+Treshold: 2.9"]
+	140363184817984 -> 140363184818048
+	140363184818176 [label="Class: 8"]
+	140363184818048 -> 140363184818176
+	140363184818240 [label="Class: 7"]
+	140363184818048 -> 140363184818240
+	140363184818304 [label="Is feature alcohol(f_id: 10) <= 12.9?
+alcohol
+Treshold: 12.9"]
+	140363184817984 -> 140363184818304
+	140363184818432 [label="Is feature pH(f_id: 8) <= 3.25?
+pH
+Treshold: 3.25"]
+	140363184818304 -> 140363184818432
+	140363184818560 [label="Is feature pH(f_id: 8) <= 3.09?
+pH
+Treshold: 3.09"]
+	140363184818432 -> 140363184818560
+	140363184818688 [label="Is feature pH(f_id: 8) <= 3.03?
+pH
+Treshold: 3.03"]
+	140363184818560 -> 140363184818688
+	140363184818816 [label="Class: 6"]
+	140363184818688 -> 140363184818816
+	140363184818880 [label="Class: 7"]
+	140363184818688 -> 140363184818880
+	140363184818944 [label="Class: 6"]
+	140363184818560 -> 140363184818944
+	140363184819008 [label="Is feature total sulfur dioxide(f_id: 6) <= 101.0?
+total sulfur dioxide
+Treshold: 101.0"]
+	140363184818432 -> 140363184819008
+	140363184819072 [label="Class: 7"]
+	140363184819008 -> 140363184819072
+	140363184819136 [label="Is feature sulphates(f_id: 9) <= 0.46?
+sulphates
+Treshold: 0.46"]
+	140363184819008 -> 140363184819136
+	140363184819264 [label="Class: 6"]
+	140363184819136 -> 140363184819264
+	140363184819328 [label="Is feature fixed acidity(f_id: 0) <= 6.0?
+fixed acidity
+Treshold: 6.0"]
+	140363184819136 -> 140363184819328
+	140363184819456 [label="Class: 7"]
+	140363184819328 -> 140363184819456
+	140363184819520 [label="Class: 8"]
+	140363184819328 -> 140363184819520
+	140363184819584 [label="Is feature chlorides(f_id: 4) <= 0.031?
+chlorides
+Treshold: 0.031"]
+	140363184818304 -> 140363184819584
+	140363184819712 [label="Class: 8"]
+	140363184819584 -> 140363184819712
+	140363184819776 [label="Class: 7"]
+	140363184819584 -> 140363184819776
+	140363184819840 [label="Class: 8"]
+	140363184817920 -> 140363184819840
+	140363184819904 [label="Is feature pH(f_id: 8) <= 2.97?
+pH
+Treshold: 2.97"]
+	140363184817856 -> 140363184819904
+	140363184820032 [label="Is feature sulphates(f_id: 9) <= 0.35?
+sulphates
+Treshold: 0.35"]
+	140363184819904 -> 140363184820032
+	140363184820160 [label="Is feature fixed acidity(f_id: 0) <= 7.3?
+fixed acidity
+Treshold: 7.3"]
+	140363184820032 -> 140363184820160
+	140363184820288 [label="Class: 8"]
+	140363184820160 -> 140363184820288
+	140363184820352 [label="Class: 6"]
+	140363184820160 -> 140363184820352
+	140363184820416 [label="Class: 6"]
+	140363184820032 -> 140363184820416
+	140363184820480 [label="Is feature pH(f_id: 8) <= 3.19?
+pH
+Treshold: 3.19"]
+	140363184819904 -> 140363184820480
+	140363184820608 [label="Is feature fixed acidity(f_id: 0) <= 6.5?
+fixed acidity
+Treshold: 6.5"]
+	140363184820480 -> 140363184820608
+	140363184820736 [label="Is feature pH(f_id: 8) <= 3.08?
+pH
+Treshold: 3.08"]
+	140363184820608 -> 140363184820736
+	140363184820864 [label="Class: 6"]
+	140363184820736 -> 140363184820864
+	140363184820928 [label="Is feature pH(f_id: 8) <= 3.12?
+pH
+Treshold: 3.12"]
+	140363184820736 -> 140363184820928
+	140363184821056 [label="Class: 7"]
+	140363184820928 -> 140363184821056
+	140363184821120 [label="Is feature sulphates(f_id: 9) <= 0.41?
+sulphates
+Treshold: 0.41"]
+	140363184820928 -> 140363184821120
+	140363184821248 [label="Class: 6"]
+	140363184821120 -> 140363184821248
+	140363184821312 [label="Class: 7"]
+	140363184821120 -> 140363184821312
+	140363184821376 [label="Is feature free sulfur dioxide(f_id: 5) <= 33.0?
+free sulfur dioxide
+Treshold: 33.0"]
+	140363184820608 -> 140363184821376
+	140363184821440 [label="Is feature total sulfur dioxide(f_id: 6) <= 99.0?
+total sulfur dioxide
+Treshold: 99.0"]
+	140363184821376 -> 140363184821440
+	140363184821504 [label="Is feature fixed acidity(f_id: 0) <= 6.7?
+fixed acidity
+Treshold: 6.7"]
+	140363184821440 -> 140363184821504
+	140363184821632 [label="Class: 7"]
+	140363184821504 -> 140363184821632
+	140363184821696 [label="Is feature residual sugar(f_id: 3) <= 3.4?
+residual sugar
+Treshold: 3.4"]
+	140363184821504 -> 140363184821696
+	140363184821824 [label="Class: 6"]
+	140363184821696 -> 140363184821824
+	140363184821888 [label="Is feature total sulfur dioxide(f_id: 6) <= 87.0?
+total sulfur dioxide
+Treshold: 87.0"]
+	140363184821696 -> 140363184821888
+	140363184821952 [label="Class: 6"]
+	140363184821888 -> 140363184821952
+	140363184822016 [label="Class: 7"]
+	140363184821888 -> 140363184822016
+	140363184822080 [label="Class: 7"]
+	140363184821440 -> 140363184822080
+	140363184822144 [label="Is feature alcohol(f_id: 10) <= 12.3?
+alcohol
+Treshold: 12.3"]
+	140363184821376 -> 140363184822144
+	140363184822272 [label="Is feature pH(f_id: 8) <= 2.99?
+pH
+Treshold: 2.99"]
+	140363184822144 -> 140363184822272
+	140363184822400 [label="Class: 8"]
+	140363184822272 -> 140363184822400
+	140363184822464 [label="Class: 6"]
+	140363184822272 -> 140363184822464
+	140363184822528 [label="Is feature fixed acidity(f_id: 0) <= 7.4?
+fixed acidity
+Treshold: 7.4"]
+	140363184822144 -> 140363184822528
+	140363184822656 [label="Is feature sulphates(f_id: 9) <= 0.37?
+sulphates
+Treshold: 0.37"]
+	140363184822528 -> 140363184822656
+	140363184822784 [label="Class: 6"]
+	140363184822656 -> 140363184822784
+	140363184822848 [label="Class: 7"]
+	140363184822656 -> 140363184822848
+	140363184822912 [label="Class: 8"]
+	140363184822528 -> 140363184822912
+	140363184822976 [label="Is feature residual sugar(f_id: 3) <= 1.4?
+residual sugar
+Treshold: 1.4"]
+	140363184820480 -> 140363184822976
+	140363184823104 [label="Is feature density(f_id: 7) <= 0.98918?
+density
+Treshold: 0.98918"]
+	140363184822976 -> 140363184823104
+	140363184823232 [label="Is feature alcohol(f_id: 10) <= 12.4?
+alcohol
+Treshold: 12.4"]
+	140363184823104 -> 140363184823232
+	140363184823360 [label="Class: 5"]
+	140363184823232 -> 140363184823360
+	140363184823424 [label="Class: 7"]
+	140363184823232 -> 140363184823424
+	140363184823488 [label="Class: 8"]
+	140363184823104 -> 140363184823488
+	140363184823552 [label="Is feature sulphates(f_id: 9) <= 0.35?
+sulphates
+Treshold: 0.35"]
+	140363184822976 -> 140363184823552
+	140363184823680 [label="Class: 6"]
+	140363184823552 -> 140363184823680
+	140363184823744 [label="Is feature volatile acidity(f_id: 1) <= 0.39?
+volatile acidity
+Treshold: 0.39"]
+	140363184823552 -> 140363184823744
+	140363184823808 [label="Is feature sulphates(f_id: 9) <= 0.42?
+sulphates
+Treshold: 0.42"]
+	140363184823744 -> 140363184823808
+	140363184823936 [label="Is feature citric acid(f_id: 2) <= 0.32?
+citric acid
+Treshold: 0.32"]
+	140363184823808 -> 140363184823936
+	140363184824064 [label="Class: 5"]
+	140363184823936 -> 140363184824064
+	140363184824128 [label="Class: 7"]
+	140363184823936 -> 140363184824128
+	140363184824192 [label="Class: 7"]
+	140363184823808 -> 140363184824192
+	140363184824256 [label="Is feature pH(f_id: 8) <= 3.29?
+pH
+Treshold: 3.29"]
+	140363184823744 -> 140363184824256
+	140363184824384 [label="Is feature density(f_id: 7) <= 0.98915?
+density
+Treshold: 0.98915"]
+	140363184824256 -> 140363184824384
+	140363184824512 [label="Class: 7"]
+	140363184824384 -> 140363184824512
+	140363184824576 [label="Class: 6"]
+	140363184824384 -> 140363184824576
+	140363184824640 [label="Class: 7"]
+	140363184824256 -> 140363184824640
+	140363184824704 [label="Is feature pH(f_id: 8) <= 3.41?
+pH
+Treshold: 3.41"]
+	140363184817792 -> 140363184824704
+	140363184824832 [label="Is feature residual sugar(f_id: 3) <= 5.1?
+residual sugar
+Treshold: 5.1"]
+	140363184824704 -> 140363184824832
+	140363184824960 [label="Is feature citric acid(f_id: 2) <= 0.37?
+citric acid
+Treshold: 0.37"]
+	140363184824832 -> 140363184824960
+	140363184825088 [label="Is feature chlorides(f_id: 4) <= 0.034?
+chlorides
+Treshold: 0.034"]
+	140363184824960 -> 140363184825088
+	140363184825216 [label="Is feature pH(f_id: 8) <= 3.01?
+pH
+Treshold: 3.01"]
+	140363184825088 -> 140363184825216
+	140363184825344 [label="Class: 6"]
+	140363184825216 -> 140363184825344
+	140363184825408 [label="Is feature density(f_id: 7) <= 0.98945?
+density
+Treshold: 0.98945"]
+	140363184825216 -> 140363184825408
+	140363184825536 [label="Is feature citric acid(f_id: 2) <= 0.28?
+citric acid
+Treshold: 0.28"]
+	140363184825408 -> 140363184825536
+	140363184825664 [label="Class: 6"]
+	140363184825536 -> 140363184825664
+	140363184825728 [label="Is feature alcohol(f_id: 10) <= 12.7?
+alcohol
+Treshold: 12.7"]
+	140363184825536 -> 140363184825728
+	140363184825856 [label="Class: 6"]
+	140363184825728 -> 140363184825856
+	140363184825920 [label="Class: 7"]
+	140363184825728 -> 140363184825920
+	140363184825984 [label="Class: 7"]
+	140363184825408 -> 140363184825984
+	140363184826048 [label="Is feature sulphates(f_id: 9) <= 0.46?
+sulphates
+Treshold: 0.46"]
+	140363184825088 -> 140363184826048
+	140363184826176 [label="Is feature total sulfur dioxide(f_id: 6) <= 144.0?
+total sulfur dioxide
+Treshold: 144.0"]
+	140363184826048 -> 140363184826176
+	140363184826240 [label="Is feature free sulfur dioxide(f_id: 5) <= 34.0?
+free sulfur dioxide
+Treshold: 34.0"]
+	140363184826176 -> 140363184826240
+	140363184826304 [label="Is feature total sulfur dioxide(f_id: 6) <= 137.0?
+total sulfur dioxide
+Treshold: 137.0"]
+	140363184826240 -> 140363184826304
+	140363184826368 [label="Is feature fixed acidity(f_id: 0) <= 6.4?
+fixed acidity
+Treshold: 6.4"]
+	140363184826304 -> 140363184826368
+	140363184826496 [label="Class: 6"]
+	140363184826368 -> 140363184826496
+	140363184826560 [label="Class: 7"]
+	140363184826368 -> 140363184826560
+	140363184826624 [label="Class: 7"]
+	140363184826304 -> 140363184826624
+	140363184826688 [label="Class: 7"]
+	140363184826240 -> 140363184826688
+	140363184826752 [label="Is feature volatile acidity(f_id: 1) <= 0.23?
+volatile acidity
+Treshold: 0.23"]
+	140363184826176 -> 140363184826752
+	140363184826816 [label="Is feature free sulfur dioxide(f_id: 5) <= 27.0?
+free sulfur dioxide
+Treshold: 27.0"]
+	140363184826752 -> 140363184826816
+	140363184826880 [label="Class: 6"]
+	140363184826816 -> 140363184826880
+	140363184826944 [label="Class: 7"]
+	140363184826816 -> 140363184826944
+	140363184827008 [label="Class: 6"]
+	140363184826752 -> 140363184827008
+	140363184827072 [label="Class: 6"]
+	140363184826048 -> 140363184827072
+	140363184827136 [label="Class: 6"]
+	140363184824960 -> 140363184827136
+	140363184827200 [label="Is feature chlorides(f_id: 4) <= 0.039?
+chlorides
+Treshold: 0.039"]
+	140363184824832 -> 140363184827200
+	140363184827328 [label="Is feature sulphates(f_id: 9) <= 0.32?
+sulphates
+Treshold: 0.32"]
+	140363184827200 -> 140363184827328
+	140363184827456 [label="Is feature citric acid(f_id: 2) <= 0.32?
+citric acid
+Treshold: 0.32"]
+	140363184827328 -> 140363184827456
+	140363184827584 [label="Class: 7"]
+	140363184827456 -> 140363184827584
+	140363184827648 [label="Is feature volatile acidity(f_id: 1) <= 0.28?
+volatile acidity
+Treshold: 0.28"]
+	140363184827456 -> 140363184827648
+	140363184827712 [label="Class: 6"]
+	140363184827648 -> 140363184827712
+	140363184827776 [label="Class: 8"]
+	140363184827648 -> 140363184827776
+	140363184827840 [label="Is feature free sulfur dioxide(f_id: 5) <= 24.0?
+free sulfur dioxide
+Treshold: 24.0"]
+	140363184827328 -> 140363184827840
+	140363184827904 [label="Class: 6"]
+	140363184827840 -> 140363184827904
+	140363184827968 [label="Is feature citric acid(f_id: 2) <= 0.36?
+citric acid
+Treshold: 0.36"]
+	140363184827840 -> 140363184827968
+	140363184828096 [label="Is feature density(f_id: 7) <= 0.99408?
+density
+Treshold: 0.99408"]
+	140363184827968 -> 140363184828096
+	140363184828224 [label="Class: 7"]
+	140363184828096 -> 140363184828224
+	140363184828288 [label="Class: 6"]
+	140363184828096 -> 140363184828288
+	140363184828352 [label="Is feature volatile acidity(f_id: 1) <= 0.34?
+volatile acidity
+Treshold: 0.34"]
+	140363184827968 -> 140363184828352
+	140363184828416 [label="Class: 6"]
+	140363184828352 -> 140363184828416
+	140363184828480 [label="Class: 7"]
+	140363184828352 -> 140363184828480
+	140363184828544 [label="Class: 6"]
+	140363184827200 -> 140363184828544
+	140363184828608 [label="Is feature volatile acidity(f_id: 1) <= 0.28?
+volatile acidity
+Treshold: 0.28"]
+	140363184824704 -> 140363184828608
+	140363184828672 [label="Class: 5"]
+	140363184828608 -> 140363184828672
+	140363184828736 [label="Class: 8"]
+	140363184828608 -> 140363184828736
+	140363184828800 [label="Is feature alcohol(f_id: 10) <= 12.5?
+alcohol
+Treshold: 12.5"]
+	140363184817728 -> 140363184828800
+	140363184828928 [label="Class: 8"]
+	140363184828800 -> 140363184828928
+	140363184828992 [label="Is feature residual sugar(f_id: 3) <= 1.3?
+residual sugar
+Treshold: 1.3"]
+	140363184828800 -> 140363184828992
+	140363184829120 [label="Class: 5"]
+	140363184828992 -> 140363184829120
+	140363184829184 [label="Class: 9"]
+	140363184828992 -> 140363184829184
+	140363184829248 [label="Is feature total sulfur dioxide(f_id: 6) <= 124.0?
+total sulfur dioxide
+Treshold: 124.0"]
+	140363184817600 -> 140363184829248
+	140363184829312 [label="Is feature residual sugar(f_id: 3) <= 4.8?
+residual sugar
+Treshold: 4.8"]
+	140363184829248 -> 140363184829312
+	140363184829440 [label="Is feature fixed acidity(f_id: 0) <= 6.1?
+fixed acidity
+Treshold: 6.1"]
+	140363184829312 -> 140363184829440
+	140363184829568 [label="Is feature volatile acidity(f_id: 1) <= 0.24?
+volatile acidity
+Treshold: 0.24"]
+	140363184829440 -> 140363184829568
+	140363184829632 [label="Is feature chlorides(f_id: 4) <= 0.037?
+chlorides
+Treshold: 0.037"]
+	140363184829568 -> 140363184829632
+	140363184829760 [label="Class: 6"]
+	140363184829632 -> 140363184829760
+	140363184829824 [label="Class: 8"]
+	140363184829632 -> 140363184829824
+	140363184829888 [label="Is feature total sulfur dioxide(f_id: 6) <= 114.0?
+total sulfur dioxide
+Treshold: 114.0"]
+	140363184829568 -> 140363184829888
+	140363184829952 [label="Is feature chlorides(f_id: 4) <= 0.039?
+chlorides
+Treshold: 0.039"]
+	140363184829888 -> 140363184829952
+	140363184830080 [label="Is feature residual sugar(f_id: 3) <= 4.5?
+residual sugar
+Treshold: 4.5"]
+	140363184829952 -> 140363184830080
+	140363184830208 [label="Is feature pH(f_id: 8) <= 3.16?
+pH
+Treshold: 3.16"]
+	140363184830080 -> 140363184830208
+	140363184830336 [label="Class: 6"]
+	140363184830208 -> 140363184830336
+	140363184830400 [label="Class: 7"]
+	140363184830208 -> 140363184830400
+	140363184830464 [label="Class: 6"]
+	140363184830080 -> 140363184830464
+	140363184830528 [label="Class: 6"]
+	140363184829952 -> 140363184830528
+	140363184830592 [label="Is feature citric acid(f_id: 2) <= 0.25?
+citric acid
+Treshold: 0.25"]
+	140363184829888 -> 140363184830592
+	140363184830720 [label="Class: 6"]
+	140363184830592 -> 140363184830720
+	140363184830784 [label="Class: 8"]
+	140363184830592 -> 140363184830784
+	140363184830848 [label="Is feature pH(f_id: 8) <= 3.3?
+pH
+Treshold: 3.3"]
+	140363184829440 -> 140363184830848
+	140363184830976 [label="Is feature free sulfur dioxide(f_id: 5) <= 24.0?
+free sulfur dioxide
+Treshold: 24.0"]
+	140363184830848 -> 140363184830976
+	140363184831040 [label="Is feature pH(f_id: 8) <= 2.98?
+pH
+Treshold: 2.98"]
+	140363184830976 -> 140363184831040
+	140363184831168 [label="Class: 8"]
+	140363184831040 -> 140363184831168
+	140363184831232 [label="Is feature citric acid(f_id: 2) <= 0.26?
+citric acid
+Treshold: 0.26"]
+	140363184831040 -> 140363184831232
+	140363184831360 [label="Class: 6"]
+	140363184831232 -> 140363184831360
+	140363184831424 [label="Is feature volatile acidity(f_id: 1) <= 0.28?
+volatile acidity
+Treshold: 0.28"]
+	140363184831232 -> 140363184831424
+	140363184831488 [label="Is feature volatile acidity(f_id: 1) <= 0.19?
+volatile acidity
+Treshold: 0.19"]
+	140363184831424 -> 140363184831488
+	140363184831552 [label="Class: 7"]
+	140363184831488 -> 140363184831552
+	140363184831616 [label="Class: 6"]
+	140363184831488 -> 140363184831616
+	140363184831680 [label="Class: 7"]
+	140363184831424 -> 140363184831680
+	140363184831744 [label="Is feature chlorides(f_id: 4) <= 0.046?
+chlorides
+Treshold: 0.046"]
+	140363184830976 -> 140363184831744
+	140363184831872 [label="Is feature total sulfur dioxide(f_id: 6) <= 95.0?
+total sulfur dioxide
+Treshold: 95.0"]
+	140363184831744 -> 140363184831872
+	140363184831936 [label="Class: 7"]
+	140363184831872 -> 140363184831936
+	140363184832000 [label="Is feature sulphates(f_id: 9) <= 0.56?
+sulphates
+Treshold: 0.56"]
+	140363184831872 -> 140363184832000
+	140363184832128 [label="Is feature residual sugar(f_id: 3) <= 0.8?
+residual sugar
+Treshold: 0.8"]
+	140363184832000 -> 140363184832128
+	140363184832256 [label="Class: 8"]
+	140363184832128 -> 140363184832256
+	140363184832320 [label="Class: 7"]
+	140363184832128 -> 140363184832320
+	140363184832384 [label="Is feature residual sugar(f_id: 3) <= 1.9?
+residual sugar
+Treshold: 1.9"]
+	140363184832000 -> 140363184832384
+	140363184881728 [label="Class: 7"]
+	140363184832384 -> 140363184881728
+	140363184881792 [label="Class: 8"]
+	140363184832384 -> 140363184881792
+	140363184881856 [label="Class: 8"]
+	140363184831744 -> 140363184881856
+	140363184881920 [label="Is feature chlorides(f_id: 4) <= 0.025?
+chlorides
+Treshold: 0.025"]
+	140363184830848 -> 140363184881920
+	140363184882048 [label="Is feature total sulfur dioxide(f_id: 6) <= 85.0?
+total sulfur dioxide
+Treshold: 85.0"]
+	140363184881920 -> 140363184882048
+	140363184882112 [label="Class: 9"]
+	140363184882048 -> 140363184882112
+	140363184882176 [label="Class: 6"]
+	140363184882048 -> 140363184882176
+	140363184882240 [label="Is feature sulphates(f_id: 9) <= 0.6?
+sulphates
+Treshold: 0.6"]
+	140363184881920 -> 140363184882240
+	140363184882368 [label="Class: 8"]
+	140363184882240 -> 140363184882368
+	140363184882432 [label="Class: 7"]
+	140363184882240 -> 140363184882432
+	140363184882496 [label="Is feature sulphates(f_id: 9) <= 0.72?
+sulphates
+Treshold: 0.72"]
+	140363184829312 -> 140363184882496
+	140363184882624 [label="Class: 7"]
+	140363184882496 -> 140363184882624
+	140363184882688 [label="Is feature density(f_id: 7) <= 0.99168?
+density
+Treshold: 0.99168"]
+	140363184882496 -> 140363184882688
+	140363184882816 [label="Class: 7"]
+	140363184882688 -> 140363184882816
+	140363184882880 [label="Is feature fixed acidity(f_id: 0) <= 6.6?
+fixed acidity
+Treshold: 6.6"]
+	140363184882688 -> 140363184882880
+	140363184883008 [label="Class: 8"]
+	140363184882880 -> 140363184883008
+	140363184883072 [label="Class: 5"]
+	140363184882880 -> 140363184883072
+	140363184883136 [label="Is feature chlorides(f_id: 4) <= 0.032?
+chlorides
+Treshold: 0.032"]
+	140363184829248 -> 140363184883136
+	140363184883264 [label="Is feature fixed acidity(f_id: 0) <= 6.0?
+fixed acidity
+Treshold: 6.0"]
+	140363184883136 -> 140363184883264
+	140363184883392 [label="Is feature citric acid(f_id: 2) <= 0.31?
+citric acid
+Treshold: 0.31"]
+	140363184883264 -> 140363184883392
+	140363184883520 [label="Class: 8"]
+	140363184883392 -> 140363184883520
+	140363184883584 [label="Class: 7"]
+	140363184883392 -> 140363184883584
+	140363184883648 [label="Is feature citric acid(f_id: 2) <= 0.34?
+citric acid
+Treshold: 0.34"]
+	140363184883264 -> 140363184883648
+	140363184883776 [label="Class: 7"]
+	140363184883648 -> 140363184883776
+	140363184883840 [label="Class: 9"]
+	140363184883648 -> 140363184883840
+	140363184883904 [label="Is feature density(f_id: 7) <= 0.9912?
+density
+Treshold: 0.9912"]
+	140363184883136 -> 140363184883904
+	140363184884032 [label="Is feature chlorides(f_id: 4) <= 0.035?
+chlorides
+Treshold: 0.035"]
+	140363184883904 -> 140363184884032
+	140363184884160 [label="Is feature citric acid(f_id: 2) <= 0.31?
+citric acid
+Treshold: 0.31"]
+	140363184884032 -> 140363184884160
+	140363184884288 [label="Class: 6"]
+	140363184884160 -> 140363184884288
+	140363184884352 [label="Class: 8"]
+	140363184884160 -> 140363184884352
+	140363184884416 [label="Is feature density(f_id: 7) <= 0.98856?
+density
+Treshold: 0.98856"]
+	140363184884032 -> 140363184884416
+	140363184884544 [label="Class: 8"]
+	140363184884416 -> 140363184884544
+	140363184884608 [label="Is feature alcohol(f_id: 10) <= 12.4?
+alcohol
+Treshold: 12.4"]
+	140363184884416 -> 140363184884608
+	140363184884736 [label="Class: 7"]
+	140363184884608 -> 140363184884736
+	140363184884800 [label="Is feature citric acid(f_id: 2) <= 0.34?
+citric acid
+Treshold: 0.34"]
+	140363184884608 -> 140363184884800
+	140363184884928 [label="Is feature sulphates(f_id: 9) <= 0.55?
+sulphates
+Treshold: 0.55"]
+	140363184884800 -> 140363184884928
+	140363184885056 [label="Class: 7"]
+	140363184884928 -> 140363184885056
+	140363184885120 [label="Is feature residual sugar(f_id: 3) <= 2.0?
+residual sugar
+Treshold: 2.0"]
+	140363184884928 -> 140363184885120
+	140363184885248 [label="Class: 7"]
+	140363184885120 -> 140363184885248
+	140363184885312 [label="Class: 6"]
+	140363184885120 -> 140363184885312
+	140363184885376 [label="Class: 6"]
+	140363184884800 -> 140363184885376
+	140363184885440 [label="Is feature pH(f_id: 8) <= 3.17?
+pH
+Treshold: 3.17"]
+	140363184883904 -> 140363184885440
+	140363184885568 [label="Class: 6"]
+	140363184885440 -> 140363184885568
+	140363184885632 [label="Is feature total sulfur dioxide(f_id: 6) <= 155.0?
+total sulfur dioxide
+Treshold: 155.0"]
+	140363184885440 -> 140363184885632
+	140363184885696 [label="Is feature citric acid(f_id: 2) <= 0.27?
+citric acid
+Treshold: 0.27"]
+	140363184885632 -> 140363184885696
+	140363184885824 [label="Is feature sulphates(f_id: 9) <= 0.49?
+sulphates
+Treshold: 0.49"]
+	140363184885696 -> 140363184885824
+	140363184885952 [label="Class: 6"]
+	140363184885824 -> 140363184885952
+	140363184886016 [label="Class: 8"]
+	140363184885824 -> 140363184886016
+	140363184886080 [label="Class: 8"]
+	140363184885696 -> 140363184886080
+	140363184886144 [label="Class: 6"]
+	140363184885632 -> 140363184886144
+	140363184886208 [label="Is feature residual sugar(f_id: 3) <= 4.7?
+residual sugar
+Treshold: 4.7"]
+	140363184765120 -> 140363184886208
+	140363184886336 [label="Is feature free sulfur dioxide(f_id: 5) <= 21.0?
+free sulfur dioxide
+Treshold: 21.0"]
+	140363184886208 -> 140363184886336
+	140363184886400 [label="Is feature alcohol(f_id: 10) <= 12.4?
+alcohol
+Treshold: 12.4"]
+	140363184886336 -> 140363184886400
+	140363184886528 [label="Is feature total sulfur dioxide(f_id: 6) <= 94.0?
+total sulfur dioxide
+Treshold: 94.0"]
+	140363184886400 -> 140363184886528
+	140363184886592 [label="Class: 6"]
+	140363184886528 -> 140363184886592
+	140363184886656 [label="Class: 3"]
+	140363184886528 -> 140363184886656
+	140363184886720 [label="Is feature fixed acidity(f_id: 0) <= 5.8?
+fixed acidity
+Treshold: 5.8"]
+	140363184886400 -> 140363184886720
+	140363184886848 [label="Class: 7"]
+	140363184886720 -> 140363184886848
+	140363184886912 [label="Class: 5"]
+	140363184886720 -> 140363184886912
+	140363184886976 [label="Is feature pH(f_id: 8) <= 3.32?
+pH
+Treshold: 3.32"]
+	140363184886336 -> 140363184886976
+	140363184887104 [label="Is feature chlorides(f_id: 4) <= 0.04?
+chlorides
+Treshold: 0.04"]
+	140363184886976 -> 140363184887104
+	140363184887232 [label="Is feature chlorides(f_id: 4) <= 0.035?
+chlorides
+Treshold: 0.035"]
+	140363184887104 -> 140363184887232
+	140363184887360 [label="Is feature alcohol(f_id: 10) <= 12.3?
+alcohol
+Treshold: 12.3"]
+	140363184887232 -> 140363184887360
+	140363184887488 [label="Is feature sulphates(f_id: 9) <= 0.48?
+sulphates
+Treshold: 0.48"]
+	140363184887360 -> 140363184887488
+	140363184887616 [label="Is feature fixed acidity(f_id: 0) <= 6.7?
+fixed acidity
+Treshold: 6.7"]
+	140363184887488 -> 140363184887616
+	140363184887744 [label="Class: 6"]
+	140363184887616 -> 140363184887744
+	140363184887808 [label="Class: 7"]
+	140363184887616 -> 140363184887808
+	140363184887872 [label="Class: 6"]
+	140363184887488 -> 140363184887872
+	140363184887936 [label="Class: 6"]
+	140363184887360 -> 140363184887936
+	140363184888000 [label="Class: 7"]
+	140363184887232 -> 140363184888000
+	140363184888064 [label="Class: 6"]
+	140363184887104 -> 140363184888064
+	140363184888128 [label="Is feature alcohol(f_id: 10) <= 12.2?
+alcohol
+Treshold: 12.2"]
+	140363184886976 -> 140363184888128
+	140363184888256 [label="Class: 6"]
+	140363184888128 -> 140363184888256
+	140363184888320 [label="Is feature total sulfur dioxide(f_id: 6) <= 75.0?
+total sulfur dioxide
+Treshold: 75.0"]
+	140363184888128 -> 140363184888320
+	140363184888384 [label="Class: 8"]
+	140363184888320 -> 140363184888384
+	140363184888448 [label="Class: 9"]
+	140363184888320 -> 140363184888448
+	140363184888512 [label="Is feature free sulfur dioxide(f_id: 5) <= 23.0?
+free sulfur dioxide
+Treshold: 23.0"]
+	140363184886208 -> 140363184888512
+	140363184888576 [label="Is feature volatile acidity(f_id: 1) <= 0.32?
+volatile acidity
+Treshold: 0.32"]
+	140363184888512 -> 140363184888576
+	140363184888640 [label="Class: 4"]
+	140363184888576 -> 140363184888640
+	140363184888704 [label="Class: 6"]
+	140363184888576 -> 140363184888704
+	140363184888768 [label="Is feature pH(f_id: 8) <= 2.96?
+pH
+Treshold: 2.96"]
+	140363184888512 -> 140363184888768
+	140363184888896 [label="Class: 6"]
+	140363184888768 -> 140363184888896
+	140363184888960 [label="Is feature alcohol(f_id: 10) <= 12.2?
+alcohol
+Treshold: 12.2"]
+	140363184888768 -> 140363184888960
+	140363184889088 [label="Is feature free sulfur dioxide(f_id: 5) <= 24.0?
+free sulfur dioxide
+Treshold: 24.0"]
+	140363184888960 -> 140363184889088
+	140363184889152 [label="Class: 8"]
+	140363184889088 -> 140363184889152
+	140363184889216 [label="Class: 6"]
+	140363184889088 -> 140363184889216
+	140363184889280 [label="Is feature volatile acidity(f_id: 1) <= 0.28?
+volatile acidity
+Treshold: 0.28"]
+	140363184888960 -> 140363184889280
+	140363184889344 [label="Class: 8"]
+	140363184889280 -> 140363184889344
+	140363184889408 [label="Is feature citric acid(f_id: 2) <= 0.45?
+citric acid
+Treshold: 0.45"]
+	140363184889280 -> 140363184889408
+	140363184889536 [label="Class: 7"]
+	140363184889408 -> 140363184889536
+	140363184889600 [label="Is feature volatile acidity(f_id: 1) <= 0.33?
+volatile acidity
+Treshold: 0.33"]
+	140363184889408 -> 140363184889600
+	140363184889664 [label="Is feature density(f_id: 7) <= 0.9909?
+density
+Treshold: 0.9909"]
+	140363184889600 -> 140363184889664
+	140363184889792 [label="Class: 8"]
+	140363184889664 -> 140363184889792
+	140363184889856 [label="Class: 7"]
+	140363184889664 -> 140363184889856
+	140363184889920 [label="Class: 8"]
+	140363184889600 -> 140363184889920
+}
diff --git a/notebooks/decision_tree.png b/notebooks/decision_tree.png
new file mode 100644
index 0000000..2a240d8
Binary files /dev/null and b/notebooks/decision_tree.png differ
diff --git a/notebooks/visual_response.ipynb b/notebooks/visual_response.ipynb
index f8e6dcf..9742ce2 100644
--- a/notebooks/visual_response.ipynb
+++ b/notebooks/visual_response.ipynb
@@ -4,9 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# K-Means\n",
-    "\n",
-    "### Disclaimer: nur numerische Werte - sorry G"
+    "# K-Means"
    ]
   },
   {
@@ -18,20 +16,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 269,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import requests\n",
     "import json\n",
@@ -98,20 +85,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 270,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Rq6++2q6+9957T5WVlfrDH/6g448/Xl//+tf14IMP6oknntAnn3wSm4HrADPdAAAAAJCkKtfWa8bialn7HN/ib9GMxdWaN+VYlY7JielrNjY2qrKyUnfccYcOPPDAds8PGDAg6r5nzpyp1tZWrVy5UgceeKBqamrUv39/5ebm6umnn9Z5552nDz74QF6vt22f7Dlz5mjx4sV66KGHNGrUKK1cuVJTpkzR4MGDNWHChLa+Z8+erV/96lcqKCjQwIED27326tWrNWDAAI0bN67t2Omnny6326033nhD3/72t6P+urpC6AYAAACAJBQMWapYWtMucEuSJcklqWJpjSYVDo3ppebr16+XZVk64ogjYtbnHhs3btR5552no446SpJUUFDQ9lxWVpYkKTs7uy3Y79q1S3feeaf+8Y9/qLi4uO1zXn31VT388MNhofuXv/ylJk2a1Olrb9myRdnZ2WHH0tLSlJWVpS1btsTk6+sIoRsAAAAAklBVbaPq/S2dPm9Jqve3qKq2UcWHDIrZ61pWRzE/Nq6++mrNmDFDy5cv1+mnn67zzjtPY8eO7bT9+vXrtWPHjnZhurW1Vcccc0zYsb1nsJMJoRsAAAAAklBDU+eBO5p2kRo1apRcLle3F0tzu+0lw/YO7V988UVYmx/84Ac688wz9dxzz2n58uWaM2eO7r33Xl111VUd9rlnZfHnnntOw4cPD3uuT58+YY87uhR+b0OHDlVDQ0PYsd27d6uxsVFDhw7t8nN7goXUAAAAACAJZWdmxLRdpLKysnTmmWdq7ty5+vzzz9s939mWXoMHD5Yk1dd/tcDbmjVr2rXLzc3Vj370I/3lL3/Rddddp/nz50uS0tPTJUnBYLCtbWFhofr06aONGzfq0EMPDfvIzc3t1tdVXFyszz77TG+//XbbsX/+858KhUI6/vjju9VXdxC6AQAAACAJFeVnKceXoc7u1nZJyvHZ24fF2ty5cxUMBlVUVKSnn35a69at03vvvacHHnig7d7qfe0JwrfeeqvWrVun5557Tvfee29Ym2uuuUbPP/+8amtrVV1drZdeekmjR4+WJOXl5cnlculvf/ubtm3bpubmZmVmZur666/XT37yEz366KP68MMPVV1drQcffFCPPvpot76m0aNHq7S0VNOnT1dVVZVee+01zZo1S9/73vc0bNiw6AYqAoRuAAAAAEhCHrdL5WWFktQueO95XF5WaGS/7oKCAlVXV+uUU07RddddpzFjxmjSpEl68cUXNW/evA4/54ADDtDjjz+u999/X2PHjtXdd9+t22+/PaxNMBjUzJkz2wLwYYcdpt/97neSpOHDh6uiokKzZ8/WkCFDNGvWLEnSbbfdpptvvllz5sxp+7znnntO+fn53f66/vSnP+mII47QaaedprPOOktf//rX9fvf/77b/XSHyzJ5l3ySCQQC8vl88vv98nq9iS4HAAAAQIpraWlRbW2t8vPzlZER3WXglWvrVbG0JmxRtRxfhsrLCmO+XRjCdfX+RZovWUgNAAAAAJJY6ZgcTSocqqraRjU0tSg7076k3MQMN2KP0A0AAAAASc7jdsV0WzDED6EbAJC0giGLv+oDAABHI3QDAJIS968BAIBUwOrlAICkU7m2XjMWV4cFbkna4m/RjMXVqlxb38lnAgAAJBdCNwAgqQRDliqW1qijrTX2HKtYWqNgqNdsvgEAAByM0A0ASCpVtY3tZrj3Zkmq97eoqrYxfkUBAABEidANAEgqDU2dB+5o2gEAACQSoRsAkFSyMzNi2g4AACCRCN0AgKRSlJ+lHF+GOtsYzCV7FfOi/Kx4lgUAADrgcrn07LPPJrqMpEboBgAkFY/bpfKyQklqF7z3PC4vK2S/bgAADNuyZYuuuuoqFRQUqE+fPsrNzVVZWZlefPFFI6+3YsUKuVwuffbZZ0b6l6Q77rhDJSUl6tevnwYMGGDsdfZG6AYAJJ3SMTmaN+VYDfWFX0I+1JeheVOOZZ9uAEDvEwpKta9I7z5l/zcUNPpydXV1Ou644/TPf/5T99xzj959911VVlbqlFNO0cyZM42+dk9ZlqXdu3d3+Fxra6vOP/98zZgxI271ELoBAEmpdEyOXr3hVD0+/QT95ntf0+PTT9CrN5xK4AYA9D41S6T7x0iPflN6+nL7v/ePsY8bcuWVV8rlcqmqqkrnnXeeDjvsMB155JG69tpr9frrr3f4OR3NVK9Zs0Yul0t1dXWSpA0bNqisrEwDBw7UgQceqCOPPFLLli1TXV2dTjnlFEnSwIED5XK5dOmll0qSQqGQ5syZo/z8fPXt21dHH320nnrqqXav+/e//13HHXec+vTpo1dffbXDGisqKvSTn/xERx11VM8HKUJpcXslAAC6yeN2qfiQQYkuAwCAxKlZIj05TfammXsJ1NvHL1goFU6O6Us2NjaqsrJSd9xxhw488MB2z/fksuyZM2eqtbVVK1eu1IEHHqiamhr1799fubm5evrpp3Xeeefpgw8+kNfrVd++fSVJc+bM0eLFi/XQQw9p1KhRWrlypaZMmaLBgwdrwoQJbX3Pnj1bv/rVr1RQUKCBAwdGXWOsEboBAAAAIBmFglLlDWoXuKUvj7mkytnSEWdLbk/MXnb9+vWyLEtHHHFEzPrcY+PGjTrvvPPaZpoLCgransvKshdJzc7Obgv2u3bt0p133ql//OMfKi4ubvucV199VQ8//HBY6P7lL3+pSZMmxbzmniJ0AwAAAEAy2rBKCnzSRQNLCmy22+WfFLOXtayOQn5sXH311ZoxY4aWL1+u008/Xeedd57Gjh3bafv169drx44d7cJ0a2urjjnmmLBj48aNM1JzTznqnu558+Zp7Nix8nq98nq9Ki4u1t///vdElwUAAAAAsde8NbbtIjRq1Ci5XC69//773fo8t9uOl3uH9i+++CKszQ9+8AN99NFHmjp1qt59912NGzdODz74YKd9Njc3S5Kee+45rVmzpu2jpqYm7L5uSR1eCp8MHBW6R4wYobvuuktvv/223nrrLZ166qn61re+pf/85z+JLg0AAAAAYqv/kNi2i1BWVpbOPPNMzZ07V59//nm75zvb0mvw4MGSpPr6+rZja9asadcuNzdXP/rRj/SXv/xF1113nebPny9JSk9PlyQFg1+tzF5YWKg+ffpo48aNOvTQQ8M+cnNzo/0S48pRobusrExnnXWWRo0apcMOO0x33HGH+vfv3+nqeQAAAADgWHklkneYJFcnDVySd7jdLsbmzp2rYDCooqIiPf3001q3bp3ee+89PfDAA233Vu9rTxC+9dZbtW7dOj333HO69957w9pcc801ev7551VbW6vq6mq99NJLGj16tCQpLy9PLpdLf/vb37Rt2zY1NzcrMzNT119/vX7yk5/o0Ucf1Ycffqjq6mo9+OCDevTRR7v9dW3cuFFr1qzRxo0bFQwG22bO98yom+Co0L23YDCoJ554Qp9//nmnbzoAAAAAOJbbI5Xe/eWDfYP3l49L74rpImp7FBQUqLq6Wqeccoquu+46jRkzRpMmTdKLL76oefPmdfg5BxxwgB5//HG9//77Gjt2rO6++27dfvvtYW2CwaBmzpyp0aNHq7S0VIcddph+97vfSZKGDx+uiooKzZ49W0OGDNGsWbMkSbfddptuvvlmzZkzp+3znnvuOeXn53f767rlllt0zDHHqLy8XM3NzTrmmGN0zDHH6K233up2X5FyWSbvkjfg3XffVXFxsVpaWtS/f3899thjOuusszpsu2vXLu3atavtcSAQUG5urvx+v7xeb7xKBgAAANBLtbS0qLa2Vvn5+crIyIiuk5ol9irmey+q5h1uB+4YbxeGcF29f4FAQD6fb7/50nGrlx9++OFas2aN/H6/nnrqKV1yySV6+eWXVVhY2K7tnDlzVFFRkYAqAQAAACBGCifb24JtWGUvmtZ/iH1JuYEZbsSe42a693X66afrkEMO0cMPP9zuOWa6AQAAACRSTGa6kTC9cqZ7X6FQKCxY761Pnz7q06dPnCsCAAAAAMDmqNB944036hvf+IYOPvhgNTU16bHHHtOKFSv0/PPPJ7o0AAAAAADacVTobmho0LRp01RfXy+fz6exY8fq+eef16RJkxJdGgAAAAAA7TgqdP/f//1foksAAAAAgG5z+FJavVYoFOpxH44K3QAAAADgJAcccIBcLpe2bdumwYMHy+Xad79tJCPLstTa2qpt27bJ7XYrPT096r4I3QAAAABgiMfj0YgRI/Txxx+rrq4u0eWgm/r166eDDz5Ybrc76j4I3QAAAABgUP/+/TVq1Ch98cUXiS4F3eDxeJSWltbjqxMI3QAAAABgmMfjkcfjSXQZSIDo58gBAAAAAECXCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABD0hJdAADAvGDIUlVtoxqaWpSdmaGi/Cx53K5ElwUAAJDyCN0AkOIq19arYmmN6v0tbcdyfBkqLytU6ZicBFYGAACQ+ri8HABSWOXaes1YXB0WuCVpi79FMxZXq3JtfYIqAwAA6B0I3QCQooIhSxVLa2R18NyeYxVLaxQMddQCAAAAsUDoBoAUVVXb2G6Ge2+WpHp/i6pqG+NXFAAAQC9D6AaAFNXQ1HngjqYdAAAAuo/QDQApKjszI6btAAAA0H2EbgBIUUX5WcrxZaizjcFcslcxL8rPimdZAAAAvQqhGwBSlMftUnlZoSS1C957HpeXFbJfNwAAgEGEbgBIYaVjcjRvyrEa6gu/hHyoL0PzphzLPt0AAACGpSW6AACAWaVjcjSpcKiqahvV0NSi7Ez7knJmuAEAAMwjdANAL+Bxu1R8yKBElwEAANDrcHk5AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQ9ISXQAAxFIwZKmqtlENTS3KzsxQUX6WPG5XossCAABAL0XoBpAyKtfWq2Jpjer9LW3HcnwZKi8rVOmYnARWBgAAgN6Ky8sBpITKtfWasbg6LHBL0hZ/i2Ysrlbl2voEVQYAAIDejNANwPGCIUsVS2tkdfDcnmMVS2sUDHXUAgAAADCH0A3A8apqG9vNcO/NklTvb1FVbWP8igIAAABE6AaQAhqaOg/c0bQDAAAAYoXQDcDxsjMzYtoOAAAAiBVCNwDHK8rPUo4vQ51tDOaSvYp5UX5WPMsCAAAACN0AnM/jdqm8rFCS2gXvPY/LywrZrxsAAABxR+gGkBJKx+Ro3pRjNdQXfgn5UF+G5k05ln26AQAAkBBpiS4AAGKldEyOJhUOVVVtoxqaWpSdaV9Szgw3AAAAEoXQDSCleNwuFR8yKNFlAAAAAJK4vBwAAAAAAGMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAxJS3QBAAAkQjBkqaq2UQ1NLcrOzFBRfpY8bleiywIAACmG0A0A6HUq19arYmmN6v0tbcdyfBkqLytU6ZicBFYGAABSDZeXAwB6lcq19ZqxuDoscEvSFn+LZiyuVuXa+gRVBgAAUhGhGwDQawRDliqW1sjq4Lk9xyqW1igY6qgFAABA9xG6AQC9RlVtY7sZ7r1Zkur9LaqqbYxfUQAAIKURugEAvUZDU+eBO5p2AAAA+0PoBgD0GtmZGTFtBwAAsD+EbgBAr1GUn6UcX4Y62xjMJXsV86L8rHiWBQAAUhihGwDQa3jcLpWXFUpSu+C953F5WSH7dQMAgJghdAMAepXSMTmaN+VYDfWFX0I+1JeheVOOZZ9uAAAQU2mJLgAAgHgrHZOjSYVDVVXbqIamFmVn2peUM8MNAABijdANAOiVPG6Xig8ZlOgyAABAiuPycgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYIhjQvecOXM0fvx4ZWZmKjs7W+ecc44++OCDRJcFAAAAAECnHBO6X375Zc2cOVOvv/66XnjhBX3xxRc644wz9Pnnnye6NAAAAAAAOuSyLMtKdBHR2LZtm7Kzs/Xyyy/r5JNPjuhzAoGAfD6f/H6/vF6v4QoBAAAAAKkq0nzpmJnuffn9fklSVlZWgisBAAAAAKBjaYkuIBqhUEjXXHONTjzxRI0ZM6bTdrt27dKuXbvaHgcCgXiUBwAAAACAJIeG7pkzZ2rt2rV69dVXu2w3Z84cVVRUxKkqAIhe6+6QFq2u04bGHcrL6qepxSOVnubYi5EAAADwJcfd0z1r1iz99a9/1cqVK5Wfn99l245munNzc7mnG0BSmbOsRvNfqVVor5/Gbpc0/aR83XhWYeIKAwAAQKcivafbMTPdlmXpqquu0jPPPKMVK1bsN3BLUp8+fdSnT584VAcA0ZmzrEYPr6xtdzxkqe04wRsAAMC5HHPt4syZM7V48WI99thjyszM1JYtW7Rlyxbt3Lkz0aUBQFRad4c0/5X2gXtv81+pVevuUJwqAgAAQKw5JnTPmzdPfr9fEydOVE5OTtvHn//850SXBgBRWbS6LuyS8o6ELLsdAAAAnMlRl5cDQCrZ0Lgjpu0AAACQfBwz0w0AqSYvq19M2wEAACD5ELoBIEGmFo+U29V1G7fLbgcAAABnInQDQIKkp7k1/aSud2KYflI++3UDAAA4mGPu6QaAVLRnOzD26QYAAEhNLqsXrVAW6eblABBvrbtDWrS6Thsadygvq5+mFo9khhsAACCJRZovmekGgCSQnubW5ScVJLoMAAAAxBjTKAAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAxJS3QBAHqfna1B3bmsRnWf7tDIQf3087MK1Tfdk+iykISCIUtVtY1qaGpRdmaGivKz5HG7El0WAADJLxSUNqySmrdK/YdIeSWS2wG/bzm17i4QugHE1fSFb+qFmoa2x6+skxa9vlGTCrM1f9r4BFaGZFO5tl4VS2tU729pO5bjy1B5WaFKx+QksDIAAJJczRKp8gYp8MlXx7zDpNK7pcLJiatrf5xa935weTmAuNk3cO/thZoGTV/4ZpwrQrKqXFuvGYurwwK3JG3xt2jG4mpVrq1PUGUAACS5miXSk9PCg6skBert4zVLElPX/ji17ggQugHExc7WYKeBe48Xahq0szUYp4qQrIIhSxVLa2R18NyeYxVLaxQMddQCAIBeLBS0Z4q7+le0crbdLpk4te4IEboBxMWdy2pi2g6pq6q2sd0M994sSfX+FlXVNsavKAAAnGDDqvYzxWEsKbDZbpdMnFp3hAjdAOKi7tMdMW2H1NXQ1HngjqYdAAC9RvPW2LaLF6fWHSFCN4C4GDmoX0zbIXVlZ2bEtB0AAL1G/yGxbRcvTq07QoRuAHHx87MKY9oOqasoP0s5vgx1tjGYS/Yq5kX5WfEsCwCA5JdXYq/23dW/ot7hdrtk4tS6I0ToBhAXfdM9mlSY3WWbSYXZ7NcNedwulZfZf3zZ95/ePY/LywrZrxsAgH25Pfb2WpI6/Ve09K7k2/faqXVHiNANIG7mTxvfafBmn27srXRMjuZNOVZDfeGXkA/1ZWjelGPZpxsAgM4UTpYuWCh59/m30jvMPp6s+107te4IuCzL6jV7rgQCAfl8Pvn9fnm93kSXA/RaO1uDunNZjeo+3aGRg/rp52cVMsONDgVDlqpqG9XQ1KLsTPuScma4AQCIQChor/bdvNW+FzqvxBkzxQ6qO9J8SegGAAAAAKCbIs2XXF4OAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGpCW6AAC9TzBkqaq2UQ1NLcrOzFBRfpY8bleiy9ovk3U7dUwAAADQNUI3gLiqXFuviqU1qve3tB3L8WWovKxQpWNyElhZ10zW7dQxAQAAwP5xeTmAuKlcW68Zi6vDwqUkbfG3aMbialWurU9QZV0zWbdTxwQAAACRIXQDiItgyFLF0hpZHTy351jF0hoFQx21SByTdTt1TAAAABA5QjeAuKiqbWw3m7s3S1K9v0VVtY3xKyoCJut26pgAAAAgcoRuAHHR0NR5uIymXbyYrNupYwIAAIDIEboBxEV2ZkZM28WLybqdOiYAAACIHKEbQFwU5Wcpx5ehzjbBcslesbsoPyueZe2XybqdOiYAAACIHKEbQFx43C6VlxVKUruQuedxeVlh0u1NbbJup44JAAAAIkfoBhA3pWNyNG/KsRrqC79ceqgvQ/OmHJu0e1KbrNupYwIAAIDIuCzL6jV70QQCAfl8Pvn9fnm93kSXA/RawZClqtpGNTS1KDvTvnzaCbO5Jut26pgAAAD0VpHmy7Q41gQAkuzLqosPGZToMrrNZN1OHRMAAAB0jcvLAQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAkLREFwDsTzBkqaq2UQ1NLcrOzFBRfpY8bleiy0ppjDkAAL1QKChtWCU1b5X6D5HySiS3J9FVAY5H6EZSq1xbr4qlNar3t7Qdy/FlqLysUKVjchJYWepizAEA6IVqlkiVN0iBT7465h0mld4tFU5OXF1ACuDyciStyrX1mrG4Oiz8SdIWf4tmLK5W5dr6BFWWuhhzAAB6oZol0pPTwgO3JAXq7eM1SxJTF5AiCN1ISsGQpYqlNbI6eG7PsYqlNQqGOmqBaDDmAAD0QqGgPcPd1W8AlbPtdgCiQuhGUqqqbWw327o3S1K9v0VVtY3xKyrFMeYAAPRCG1a1n+EOY0mBzXY7AFEhdCMpNTR1Hv6iaYf9Y8wBAOiFmrfGth2AdgjdSErZmRkxbYf9Y8wBAOiF+g+JbTsA7RC6kZSK8rOU48tQZ5tUuWSvqF2UnxXPslIaYw4AQC+UV2KvUt7VbwDe4XY7AFEhdCMpedwulZcVSmr/T8Cex+VlhewdHUOMOQAAvZDbY28LJqnT3wBK72K/bqAHCN1IWqVjcjRvyrEa6gu/nHmoL0PzphzLntEGMOYAAPRChZOlCxZK3n3+nfcOs4+zTzfQIy7LsnrN/j+BQEA+n09+v19erzfR5SBCwZClqtpGNTS1KDvTvryZ2VazGHMAAHqhUNBepbx5q30Pd14JM9xAFyLNl2lxrAmIisftUvEhgxJdRq/CmAMA0Au5PVL+SYmuAkg5XF4OAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAkLREFwAg+bTuDmnR6jptaNyhvKx+mlo8Uulpsfsbncn+gyFLVbWNamhqUXZmhorys+Rxu2LSt5MxLgAAAInhsizLSnQR8RIIBOTz+eT3++X1ehNdDpCU5iyr0fxXahXa6yeD2yVNPylfN55VmNT9V66tV8XSGtX7W9qO5fgyVF5WqNIxOT3q28kYFwAAgNiLNF9yeTmANnOW1ejhleGBWJJClvTwylrNWVaTtP1Xrq3XjMXVYcFSkrb4WzRjcbUq19ZH3beTMS4AAACJRegGIMm+5Hv+K7Vdtpn/Sq1ad4eSrv9gyFLF0hp1dNnOnmMVS2sU3DftpzjGBQAAIPEI3QAkSYtW17Wbgd5XyLLbJVv/VbWN7WZy92ZJqve3qKq2sdt9OxnjAgAAkHiOCt0rV65UWVmZhg0bJpfLpWeffTbRJQEpY0Pjjpi2i2f/DU2dB8to2qUKxgUAACDxHBW6P//8cx199NGaO3duoksBUk5eVr+Ytotn/9mZGTFtlyoYFwAAgMRzVOj+xje+odtvv13f/va3E10KkHKmFo/U/naQcrvsdsnWf1F+lnJ8Geqse5fs1bqL8rO63beTMS4AAACJ56jQ3V27du1SIBAI+wDQsfQ0t6aflN9lm+kn5Ue9n7bJ/j1ul8rL7O3G9g2Yex6XlxX2un2pGRcAAIDES+nQPWfOHPl8vraP3NzcRJcEJLUbzyrUFSfnt5uRdrukK07u+T7aJvsvHZOjeVOO1VBf+KXSQ30Zmjfl2F67HzXjAgAAkFguy7IcuVeMy+XSM888o3POOafTNrt27dKuXbvaHgcCAeXm5u5383Kgt2vdHdKi1XXa0LhDeVn9NLV4ZNQz3PHuPxiyVFXbqIamFmVn2pdOM5PLuAAAAMRaIBCQz+fbb75Mi2NNcdenTx/16dMn0WUAjpOe5tblJxU4sn+P26XiQwYZ6dvJGBcAAIDESOnLywEAAAAASCRHzXQ3Nzdr/fr1bY9ra2u1Zs0aZWVl6eCDD05gZQAAAAAAtOeo0P3WW2/plFNOaXt87bXXSpIuueQSPfLIIwmqCgAAAACAjjkqdE+cOFEOXfcNAAAAANALcU83gG7x+/36+OOPO3zu448/lt/vj3NFAAAAQPIidAOImN/vV2lpqSZMmKBNmzaFPbdp0yZNmDBBpaWlBG8AAADgS4RuABFrampSQ0ODPvroI02cOLEteG/atEkTJ07URx99pIaGBjU1NSW4UgAAACA5ELoBRGzEiBFasWKFCgoK2oL3qlWr2gJ3QUGBVqxYoREjRiS6VAAAACApuKxetDJZIBCQz+eT3++X1+tNdDmAY+09s73HnsCdm5ubwMoAAACA+Ig0XzLTDaDbcnNztWjRorBjixYtInADAAAA+yB0A+i2TZs2aerUqWHHpk6d2m5xNQAAAKC3I3QD6Ja9Ly0vKCjQa6+9FnaPN8EbAAAA+AqhG0DEPv7443aLppWUlLRbXK2zfbwBAACA3iYt0QUAcI7MzExlZ2dLUtiiabm5uVqxYoUmTpyo7OxsZWZmJrJMAAAAIGmwejmAbvH7/WpqaupwW7CPP/5YmZmZ8vl8CagMAAAAiJ9I8yUz3Uh6wZClqtpGNTS1KDszQ0X5WfK4XYkuKyKtu0NatLpOGxp3KC+rn6YWj1R6WvLf1dFV3T6fr9NQHen+3CbfU6eeL049V5AYTj3PFQpKG1ZJzVul/kOkvBLJ7Un+vgEA6AFmupHUKtfWq2Jpjer9LW3HcnwZKi8rVOmYnARWtn9zltVo/iu1Cu31HeZ2SdNPyteNZxUmrrD9MF23yffUqeeLU88VJIZTz3PVLJEqb5ACn3x1zDtMKr1bKpycvH0DANCJSPMloRtJq3JtvWYsrta+J+ieuZx5U45N2l8w5yyr0cMrazt9/oqTkzNMma7b5Hvq1PPFqecKEsOp57lqlkhPTpM6q/yChdGHY5N9AwDQhUjzJdcuIikFQ5Yqlta0+xVK+urXqoqlNQqGku9vRq27Q5r/SuchSpLmv1Kr1t2hOFUUGdN1m3xPnXq+OPVcQWI49TxXKGjPQndVeeVsu10y9Q0AQIwQupGUqmobwy6d3Jclqd7foqraxvgVFaFFq+u0v995Q5bdLpmYrtvke+rU88Wp5woSw6nnuTasCr/sux1LCmy22yVT3wAAxAihG0mpoanzXyyjaRdPGxp3xLRdvJiu2+R76tTzxannChLDqee5mrfGtl28+gYAIEYI3UhK2ZkZMW0XT3lZ/WLaLl5M123yPXXq+eLUcwWJ4dTzXP2HxLZdvPoGACBGCN1ISkX5WcrxZaizDXBcslfrLcrPimdZEZlaPFL727nH7bLbJRPTdZt8T516vjj1XEFiOPU8V16JvZJ4V5V7h9vtkqlvAABihNCNpORxu1ReZq/YvO+vUnsel5cVJuW+tOlpbk0/Kb/LNtNPyk+6PZhN123yPXXq+eLUcwWJ4dTzXG6PvXWXpE4rL70ruj21TfYNAECM8JscklbpmBzNm3KshvrCL5Uc6stI3m1xvnTjWYW64uT8drOYbldybwFlum6T76lTzxennitIDKee5yqcbG/d5d2nPu+wnm/pZbJvAABigH26kfSCIUtVtY1qaGpRdqZ96WTSzeR0onV3SItW12lD4w7lZfXT1OKRjpi1NF23yffUqeeLU88VJIZTz3OFgvZK4s1b7fus80piNwttsm8AADoQab4kdAMAAAAA0E2R5kumUQAAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGpEX7iaFQSOvXr1dDQ4NCoVDYcyeffHKPCwMAAAAAwOmiCt2vv/66LrroIm3YsEGWZYU953K5FAwGY1IcAAAAAABOFlXo/tGPfqRx48bpueeeU05OjlwuV6zrAgAAAADA8aIK3evWrdNTTz2lQw89NNb1AAAAAACQMqJaSO3444/X+vXrY10LAAAAAAApJeKZ7n//+99t/3/VVVfpuuuu05YtW3TUUUfpgAMOCGs7duzY2FUIAAAAAIBDuax9V0LrhNvtlsvlardwWltHXz6XzAupBQIB+Xw++f1+eb3eRJcDAAAAAHCoSPNlxDPdtbW1MSkMQPILhixV1TaqoalF2ZkZKsrPkscduwUTTfcPoBfa3Sq9OV/aXicNHCmNny6lpSe6qtTGmANARCKe6d7bypUrVVJSorS08My+e/durVq1Kmn36WamG9i/yrX1qlhao3p/S9uxHF+GyssKVTomJ+n7B9ALLb9ZWv1byQp9dczllopnSWfclri6UhljDgAR58uoQrfH41F9fb2ys7PDjn/66afKzs7m8nLAoSrX1mvG4mrt+0Nhzxz0vCnH9igYm+4fQC+0/GZp1QOdP19yNSEw1hhzAJAUeb6MavXyPfdu7+vTTz/VgQceGE2XABIsGLJUsbSmXSCW1HasYmmNgqFu/50uLv0D6IV2t9qzrV1ZPdduh9hgzAGg27q1T/e5554ryV407dJLL1WfPn3angsGg/r3v/+tkpKS2FYIIC6qahvDLvnelyWp3t+iqtpGFR8yKOn6B9ALvTk//PLmjlhBu13xzPjUlOoYcwDotm6Fbp/PJ8me6c7MzFTfvn3bnktPT9cJJ5yg6dOnx7ZCAHHR0NR5II6mXbz7B9ALba+LbTvsH2MOAN3WrdC9YMECSdLIkSN1/fXXcyk5kEKyMzNi2i7e/QPohQaOjG077B9jDgDdFtU93eXl5QRuIMUU5Wcpx5ehzjbucsleZbwoPysp+wfQC42fbq+Y3RWXx26H2GDMAaDbIp7pPuaYYzpcPK0j1dXVURcEIDE8bpfKywo1Y3G1XFLYgmd7vvPLywqj3k/bdP8AeqG0dHuLqq5W0i6eyd7RscSYA0C3RTzTfc455+hb3/qWvvWtb+nMM8/Uhx9+qD59+mjixImaOHGiMjIy9OGHH+rMM880WS8Ag0rH5GjelGM11Bd+ifdQX0ZMtvMy3T+AXuiM2+wtqvadfXV52LrKFMYcALolqn26f/CDHygnJ0e33Rb+Q7W8vFybNm3SH//4x5gVGEvs0w1EJhiyVFXbqIamFmVn2pd8x3IG2nT/AHqh3a32itnb6+z7icdPZ7bVNMYcQC8Xab6MKnT7fD699dZbGjVqVNjxdevWady4cfL7/d2vOA4I3QAAAACAWIg0X0a1kFrfvn312muvtTv+2muvKSODlYcBAAAAAJC6uWXYHtdcc41mzJih6upqFRUVSZLeeOMN/fGPf9TNN98c0wIBAAAAAHCqqEL37NmzVVBQoN/85jdavHixJGn06NFasGCBLrjggpgWCAAAAACAU0V1T7dTcU83AAAAACAWjN7TDQAAAAAA9i/iy8uzsrL03//+VwcddJAGDhwol6vz7X0aGxtjUhwAAAAAAE4Wcei+7777lJmZ2fb/XYVuAAAAAADAPd0AAAAAAHSb0Xu6p02bpgULFujDDz+MukAAAAAAAFJdVKE7PT1dc+bM0ahRo5Sbm6spU6boD3/4g9atWxfr+gAAAAAAcKweXV6+efNmrVy5Ui+//LJefvll/fe//1VOTo4+/vjjWNYYM1xeDgAAAACIhbhsGTZw4EANGjRIAwcO1IABA5SWlqbBgwf3pEsAAAAAAFJGVKH75z//uUpKSjRo0CDNnj1bLS0tmj17trZs2aJ33nkn1jUCAAAAAOBIUV1e7na7NXjwYP3kJz/Rueeeq8MOO8xEbTHH5eUAAAAAgFiINF9GvE/33t555x29/PLLWrFihe69916lp6drwoQJmjhxoiZOnOiYEN7bBEOWqmob1dDUouzMDBXlZ8njTv791k3X3bo7pEWr67ShcYfysvppavFIpaf16M4Lx3PquQIkjVBQ2rBKat4q9R8i5ZVIbk+iq0JP7G6V3pwvba+TBo6Uxk+X0tJj0zfnS8dMjotT+zbNybUDSSwm+3T/61//0n333ac//elPCoVCCgaDsagt5nrzTHfl2npVLK1Rvb+l7ViOL0PlZYUqHZOTwMq6ZrruOctqNP+VWoX2+i5wu6TpJ+XrxrMKe9y/Ezn1XAGSRs0SqfIGKfDJV8e8w6TSu6XCyYmrC9FbfrO0+reSFfrqmMstFc+SzritZ31zvnTM5Lg4tW/TnFw7kCCR5suoQrdlWXrnnXe0YsUKrVixQq+++qoCgYDGjh2rCRMm6L777utR8ab01tBdubZeMxZXa983es+85bwpxyZlmDJd95xlNXp4ZW2nz19xcu8L3k49V4CkUbNEenKa1Nl30QUL+eXVaZbfLK16oPPnS66OPnhzvnTM5Lg4tW/TnFw7kEBGVy/PysrS8ccfr8cee0yjRo3So48+qv/973+qrq5O2sDdWwVDliqW1rT7ESp99WO1YmmNgqEeX/AQU6brbt0d0vxXOg/ckjT/lVq17g512SaVOPVcAZJGKGjPEnX1XVQ5224HZ9jdas9wd2X1XLtdd3G+dMzkuDi1b9OcXDvgEFGF7sWLF+vTTz/VW2+9pXvvvVdlZWUaMGBAu3Yff/yxQqHeE1qSUVVtY9hlwvuyJNX7W1RV2xi/oiJguu5Fq+u0v+wYsux2vYVTzxUgaWxYFX5ZZjuWFNhst4MzvDk//JLyjlhBu113cb50zOS4OLVv05xcO+AQUYXus88+O6LLswsLC1VXVxfNSyBGGpo6D1HRtIsX03VvaNwR03apwKnnCpA0mrfGth0Sb3tdbNvtjfOlYybHxal9m+bk2gGHMLpEcwzWaEMPZWdmxLRdvJiuOy+rX0zbpQKnnitA0ug/JLbtkHgDR8a23d44Xzpmclyc2rdpTq4dcIjevS9SL1CUn6UcX4Y62+zJJXtl6qL8rHiWtV+m655aPFL72wHL7bLb9RZOPVeApJFXYq/029V3kXe43Q7OMH66vUp5V1weu113cb50zOS4OLVv05xcO+AQhO4U53G7VF5mr8C974/SPY/LywqTbg9m03Wnp7k1/aT8LttMPym/V+3X7dRzBUgabo+9tY6kTr+LSu9iz1snSUu3twXrSvHM6Pbr5nzpmMlxcWrfpjm5dsAhek+i6MVKx+Ro3pRjNdQXflnwUF9GUm8BZbruG88q1BUn57eb8Xa7eud2YZJzzxUgaRROtrfW8e7zveIdxpY7TnXGbfa2YPvOeLs8PdsuTOJ86YzJcXFq36Y5uXbAAaLapztSXq9Xa9asUUFBgamX6Jbeuk/3HsGQparaRjU0tSg7075M2Amzlqbrbt0d0qLVddrQuEN5Wf00tXhkr5rh7ohTzxUgaYSC9kq/zVvt+yDzSpglcrrdrfYq5dvr7Hu4x0+Pboa7I5wvHTM5Lk7t2zQn1w4kQKT50mjozszM1L/+9S9CNwAAAAAgpUSaL9NMFlFTU6Nhw4aZfAkAAAAAAJJWxKH73HPPjbjTv/zlL5Kk3Nzc7lcEAAAAAECKiDh0+3w+k3UAAAAAAJByIg7dCxYsMFkHAAAAAAApp3cv0QwAAAAAgEFRL6T21FNP6cknn9TGjRvV2toa9lx1dXWPCwMAAAAAwOmimul+4IEHdNlll2nIkCF65513VFRUpEGDBumjjz7SN77xjVjXCAAAAACAI0UVun/3u9/p97//vR588EGlp6frZz/7mV544QVdffXV8vv9sa4RAAAAAABHiip0b9y4USUlJZKkvn37qqmpSZI0depUPf7447GrDgAAAAAAB4sqdA8dOlSNjY2SpIMPPlivv/66JKm2tlaWZcWuOgAAAAAAHCyq0H3qqadqyZIlkqTLLrtMP/nJTzRp0iR997vf1be//e2YFggAAAAAgFO5rCimpkOhkEKhkNLS7MXPn3jiCa1atUqjRo3SFVdcofT09JgXGguBQEA+n09+v19erzfR5QAAAAAAHCrSfBlV6HYqQjcAAAAAIBYizZdR79O9fft2/d///Z/ee+89SVJhYaEuu+wyZWVlRdslAAAAAAApJap7uleuXKn8/Hw98MAD2r59u7Zv364HHnhA+fn5WrlyZaxrBAAAAADAkaK6vPyoo45ScXGx5s2bJ4/HI0kKBoO68sortWrVKr377rsxLzQWuLzcmYIhS1W1jWpoalF2ZoaK8rPkcbsSXVZETNa+szWoO5fVqO7THRo5qJ9+flah+qZ7YtI3AGAfoaC0YZXUvFXqP0TKK5HcvfxnbutO6YWbpMaPpKwCadLtUnrfRFcVGSfXDiBpGL2nu2/fvlqzZo0OP/zwsOMffPCBvva1r2nnzp3drzgOCN3OU7m2XhVLa1Tvb2k7luPLUHlZoUrH5CSwsv0zWfv0hW/qhZqGdscnFWZr/rTxPeobALCPmiVS5Q1S4JOvjnmHSaV3S4WTE1dXIj1+ofTBsvbHDz9LuvDx+NfTHU6uHUBSiTRfRnV5+bHHHtt2L/fe3nvvPR199NHRdAm0U7m2XjMWV4eFVkna4m/RjMXVqlxbn6DK9s9k7Z0Fbkl6oaZB0xe+GXXfAIB91CyRnpwWHrglKVBvH69Zkpi6Eqmz0CrZxx+/ML71dIeTawfgWFEtpHb11Vfrxz/+sdavX68TTjhBkvT6669r7ty5uuuuu/Tvf/+7re3YsWNjUyl6lWDIUsXSGnV0GYYlySWpYmmNJhUOTbpLzU3WvrM12Gng3uOFmgbtbA1yqTkA9FQoaM9wd/UTvXK2dMTZvedS89adnYfWPT5YZrdLtsu1nVw7AEeLKnRfeKH9V8Cf/exnHT7ncrlkWZZcLpeCwWDPKkSvVFXb2G6WeG+WpHp/i6pqG1V8yKD4FRYBk7Xfuawm4na3nXNUt/oGAOxjw6r2M9xhLCmw2W6Xf1LcykqoF26KvN3Z95qtpbucXDsAR4sqdNfW1sa6DiBMQ1PnoTWadvFksva6T3fEtB0AoAvNW2PbLhU0fhTbdvHk5NoBOFpUoTsvLy/WdQBhsjMzYtounkzWPnJQP72yLrJ2AIAe6j8ktu1SQVaB9OE/I2uXbJxcOwBHi2ohNUlatGiRTjzxRA0bNkwbNmyQJN1///3661//GrPi0HsV5Wcpx5ehzu54dsleCbwoPyueZUXEZO0/P6swpu0AAF3IK7FXKe/qJ7p3uN2ut5h0e2zbxZOTawfgaFGF7nnz5unaa6/VWWedpc8++6ztvu0BAwbo/vvvj2V96KU8bpfKy+zguO+vOnsel5cVJt0iapLZ2vumezSpMLvLNpMKs1lEDQBiwe2xtwWT1OlP9NK7es8iapK9wNjhZ3Xd5vCzknMhMifXDsDRogrdDz74oObPn69f/OIX8ni++odm3Lhxevfdd2NWHHq30jE5mjflWA31hV+GPdSXoXlTjk3qfbpN1j5/2vhOgzf7dANAjBVOli5YKHn3+bntHWYf7437dF/4eOfhNdn3unZy7QAcy2VZVkf7YHSpb9++ev/995WXl6fMzEz961//UkFBgdatW6exY8dq586dJmrtsUg3L0dyCYYsVdU2qqGpRdmZ9mXZyTjD3RGTte9sDerOZTWq+3SHRg7qp5+fVcgMNwCYEgraq5Q3b7Xv4c4r6V0z3B1p3Wmv9N34kX0f9KTbnTNL7OTaASSNSPNlVAup5efna82aNe0WVKusrNTo0aOj6RLolMftSrptwSJlsva+6R62BQOAeHF7es+2YJFK7+vcrbWcXDsAx4kqdF977bWaOXOmWlpaZFmWqqqq9Pjjj2vOnDn6wx/+EOsaAQAAAABwpKju6f7BD36gu+++WzfddJN27Nihiy66SA899JB+85vf6Hvf+16sawwzd+5cjRw5UhkZGTr++ONVVVVl9PUAAAAAAIhWVKF7586d+va3v61169apublZr7/+uq699lqNGDEi1vWF+fOf/6xrr71W5eXlqq6u1tFHH60zzzxTDQ0NRl8XAAAAAIBoRBW6v/Wtb2nhwoWSpNbWVk2ePFm//vWvdc4552jevHkxLXBvv/71rzV9+nRddtllKiws1EMPPaR+/frpj3/8o7HXBAAAAAAgWlGF7urqap10kr2YyFNPPaUhQ4Zow4YNWrhwoR544IGYFrhHa2ur3n77bZ1++ultx9xut04//XStXr26w8/ZtWuXAoFA2AcAAAAAAPESVejesWOHMjMzJUnLly/XueeeK7fbrRNOOEEbNmyIaYF7/O9//1MwGNSQIUPCjg8ZMkRbtmzp8HPmzJkjn8/X9pGbm2ukNgAAAAAAOhJV6D700EP17LPPatOmTXr++ed1xhlnSJIaGhqSav/rG2+8UX6/v+1j06ZNiS4JAAAAANCLRBW6b7nlFl1//fUaOXKkjj/+eBUXF0uyZ72POeaYmBa4x0EHHSSPx6OtW7eGHd+6dauGDh3a4ef06dNHXq837AMAAAAAgHiJKnR/5zvf0caNG/XWW2+psrKy7fhpp52m++67L2bF7S09PV3HHXecXnzxxbZjoVBIL774YlvoBwAAAAAgmaRF+4lDhw5tN8NcVFTU44K6cu211+qSSy7RuHHjVFRUpPvvv1+ff/65LrvsMqOvCwAAAABANKIO3Ynw3e9+V9u2bdMtt9yiLVu26Gtf+5oqKyvbLa4GAAAAAEAycFmWZSW6iHgJBALy+Xzy+/3c3w0AAAAAiFqk+TKqe7oBAAAAAMD+EboBAAAAADCE0A0AAAAAgCGOWkitNwiGLFXVNqqhqUXZmRkqys+Sx+1K+r7RsdbdIS1aXacNjTuUl9VPU4tHKj0tNn/r4lxJMaGgtGGV1LxV6j9EyiuR3J5EVxWZ3a3Sm/Ol7XXSwJHS+OlSWnps+nbquJgcE8nsuJiuHe217pReuElq/EjKKpAm3S6l941N304+V5z6/W+S6TFhzOOL97PXYCG1JFK5tl4VS2tU729pO5bjy1B5WaFKx+Qkbd/o2JxlNZr/Sq1Ce32HuV3S9JPydeNZhT3qm3MlxdQskSpvkAKffHXMO0wqvVsqnJy4uiKx/GZp9W8lK/TVMZdbKp4lnXFbz/p26riYHBPJ7LiYrh3tPX6h9MGy9scPP0u68PGe9e3kc8Wp3/8mmR4Txjy+eD9TQqT5ktCdJCrX1mvG4mrt+2bsmVucN+XYqAOPyb7RsTnLavTwytpOn7/i5OiDN+dKiqlZIj05Teps1C9YmLz/OC6/WVr1QOfPl1wd/S/fTh0Xk2MimR0X07Wjvc4C9x49Cd5OPlec+v1vkukxYczji/czZbB6uYMEQ5Yqlta0+7aQvvpWqVhao2Co+38fMdk3Ota6O6T5r3QeuCVp/iu1at0d6rJNRzhXUkwoaP8VuqtRr5xtt0s2u1vtWa6urJ5rt+sup46LyTGRzI6L6drRXuvOrgO3ZD/furP7fTv5XHHq979JpseEMY8v3s9eidCdBKpqG8Mu5d2XJane36Kq2sak6hsdW7S6TvvLpSHLbtddnCspZsOq8Mu+2rGkwGa7XbJ5c374ZaUdsYJ2u+5y6riYHBPJ7LiYrh3tvXBTbNvtzcnnilO//00yPSaMeXzxfvZKhO4k0NDUedCJpl28+kbHNjTuiGm7vXGupJjmrbFtF0/b62Lbbm9OHReTYyKZHRfTtaO9xo9i225vTj5XnPr9b5LpMWHM44v3s1cidCeB7MyMmLaLV9/oWF5Wv5i22xvnSorpPyS27eJp4MjYttubU8fF5JhIZsfFdO1oL6sgtu325uRzxanf/yaZHhPGPL54P3slQncSKMrPUo4vQ51tyOSSvXp0UX5WUvWNjk0tHqn97a7ldtntuotzJcXkldgriXY16t7hdrtkM366vVJxV1weu113OXVcTI6JZHZcTNeO9ibdHtt2e3PyueLU73+TTI8JYx5fvJ+9EqE7CXjcLpWX2StZ7/vtsedxeVlhVPskm+wbHUtPc2v6Sfldtpl+Un5U+3VzrqQYt8feukNSp6Neeldy7qmZlm5vDdSV4pnR7dnr1HExOSaS2XExXTvaS+9rr07elcPPim6/biefK079/jfJ9Jgw5vHF+9krEbqTROmYHM2bcqyG+sIv3R3qy+jxNk0m+0bHbjyrUFecnN9uxtvt6tl2YRLnSsopnGxv3eHdZ2y9w5J/S48zbrO3Btp31svl6fmWQU4dF5NjIpkdF9O1o70LH+88ePd0n24nnytO/f43yfSYMObxxfvZ67BPd5IJhixV1TaqoalF2Zn2pbyxmlk02Tc61ro7pEWr67ShcYfysvppavHIqGa4O8K5kmJCQXsl0eat9n1WeSXO+Sv07lZ7peLtdfZ9nOOnx25G1KnjYnJMJLPjYrp2tNe6016lvPEj+x7uSbdHN8PdESefK079/jfJ9Jgw5vHF++l4keZLQjcAAAAAAN0Uab7k8nIAAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGpCW6AMRPMGSpqrZRDU0tys7MUFF+ljxuV6LLAuB0oaC0YZXUvFXqP0TKK5HcnuTv28mcOuZO7Tse/Zvi1LqRejgX0YsRunuJyrX1qlhao3p/S9uxHF+GyssKVTomJ4GVAXC0miVS5Q1S4JOvjnmHSaV3S4WTk7dvJ3PqmDu173j0b4pT60bq4VxEL+eyLMtKdBHxEggE5PP55Pf75fV6E11O3FSurdeMxdXa943eM8c9b8qxBG8A3VezRHpymtTZT5cLFkb/y5TJvp3MqWPu1L7j0b8pTq0bqYdzESks0nzJPd0pLhiyVLG0pt2POemrH30VS2sUDPWav70AiIVQ0J616OqnS+Vsu10y9e1kTh1zp/Ydj/5NcWrdSD2ci4AkQnfKq6ptDLukfF+WpHp/i6pqG+NXFADn27Aq/DLBdiwpsNlul0x9O5lTx9ypfcejf1OcWjdSD+ciIInQnfIamjoP3NG0AwBJ9kI4sWwXr76dzKlj7tS+49G/KU6tG6mHcxGQROhOedmZGTFtBwCS7JVnY9kuXn07mVPH3Kl9x6N/U5xaN1IP5yIgidCd8orys5Tjy1BnG4O5ZK9iXpSfFc+yADhdXom98mxXP128w+12ydS3kzl1zJ3adzz6N8WpdSP1cC4CkgjdKc/jdqm8rFBS+x93ex6XlxWyXzeA7nF77K1eJHX606X0ruj2YDXZt5M5dcyd2nc8+jfFqXUj9XAuApII3b1C6ZgczZtyrIb6wi8hH+rLYLswANErnGxv9eLd52eId1jPt4Ax2beTOXXMndp3PPo3xal1I/VwLgLs092bBEOWqmob1dDUouxM+5JyZrgB9FgoaK8827zVvi8vryR2sxYm+3Yyp465U/uOR/+mOLVupB7ORaSgSPMloRsAACQNv9+vpqYmjRgxot1zH3/8sTIzM+Xz+RJQGQAA4SLNl1xeDgAAkoLf71dpaakmTJigTZs2hT23adMmTZgwQaWlpfL7/QmqEACA7iN0AwCApNDU1KSGhgZ99NFHmjhxYlvw3rRpkyZOnKiPPvpIDQ0NampqSnClAABEjtANAACSwogRI7RixQoVFBS0Be9Vq1a1Be6CggKtWLGiw0vPAQBIVtzTDQAAksreM9t77Ancubm5CawMAICvcE83AABwpNzcXC1atCjs2KJFiwjcAABHInQDAICksmnTJk2dOjXs2NSpU9strgYAgBMQugEAQNLY+9LygoICvfbaa2H3eBO8AQBOQ+gGAABJ4eOPP263aFpJSUm7xdU+/vjjRJcKAEDE0hJdAAAAgCRlZmYqOztbksIWTcvNzdWKFSs0ceJEZWdnKzMzM5FlAgDQLaxeDgAAkobf71dTU1OH24J9/PHHyszMlM/nS0BlAACEizRfMtMNAACShs/n6zRUsz83AMCJuKcbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhrCQGgCgZ0JBacMqqXmr1H+IlFciuT2x6Xt3q/TmfGl7nTRwpDR+upSWHpu+TdbtZE4dc9Pvp1PHxckYF0SKc6VjjEvSYMswAED0apZIlTdIgU++OuYdJpXeLRVO7lnfy2+WVv9WskJfHXO5peJZ0hm39axvk3U7mVPH3PT76dRxcTLGBZHiXOkY4xIXkeZLQjcAIDo1S6Qnp0na958Rl/2fCxZG/w/78pulVQ90/nzJ1dGHHZN1O5lTx9z0++nUcXEyxgWR4lzpGOMSN5HmS+7pBgB0Xyho/wW93T/o+upY5Wy7XXftbrVnFbuyeq7drrtM1u1kTh1z0++nU8fFyRgXRIpzpWOMS1IidAMAum/DqvBL1tqxpMBmu113vTk//DLeDrsP2u26y2TdTubUMTf9fjp1XJyMcUGkOFc6xrgkJUI3AKD7mrfGtt3ettfFtt3eTNbtZE4dc9Pvp1PHxckYF0SKc6VjjEtSInQDALqv/5DYttvbwJGxbbc3k3U7mVPH3PT76dRxcTLGBZHiXOkY45KUCN0AgO7LK7FXQd2zKEs7Lsk73G7XXeOn2ytDd8Xlsdt1l8m6ncypY276/XTquDgZ44JIca50jHFJSoRuAED3uT32tiOS2v/D/uXj0rui2w80Ld3eiqkrxTOj2yPZZN1O5tQxN/1+OnVcnIxxQaQ4VzrGuCQlQjcAIDqFk+1tR7w54ce9w3q+HckZt9lbMe07y+jy9GyLJsls3U7m1DE3/X46dVycjHFBpDhXOsa4JB326QYA9EwoaK+C2rzVvkcsryR2f0Hf3WqvDL29zr5vdvz06GYVO2Kybidz6pibfj+dOi5OxrggUpwrHWNcjIs0XxK6AQAAAADopkjzJZeXAwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIWmJLgAAYioUlDaskpq3Sv2HSHklktuT6KpSm1PHvKVZema6tL1OGjhS+vZ8KaN/oquKjFPH3CTTY8KYAwCiROgGkDpqlkiVN0iBT7465h0mld4tFU5OXF2pzKlj/vtTpE+qv3rcUCPdNVwadqz0w5cSV1cknDrmJpkeE8YcANADLsuyrEQXES+BQEA+n09+v19erzfR5QCIpZol0pPTJO37I81l/+eChfxyHGtOHfN9A/e+kjl4O3XMTTI9Jow5AKATkeZL7ukG4HyhoD0L1e6XYn11rHK23Q6x4dQxb2nuOnBL9vMtzfGppzucOuYmmR4TxhwAEAOEbgDOt2FV+GWf7VhSYLPdDrHh1DF/Znps28WTU8fcJNNjwpgDAGKA0A3A+Zq3xrYd9s+pY769Lrbt4smpY26S6TFhzAEAMUDoBuB8/YfEth32z6ljPnBkbNvFk1PH3CTTY8KYAwBigNANwPnySuyVhPcsbNSOS/IOt9shNpw65t+eH9t28eTUMTfJ9Jgw5gCAGCB0A3A+t8feukdS+1+Ov3xcehd76saSU8c8o7+9OnlXhh2bnPt1O3XMTTI9Jow5ACAGCN0AUkPhZHvrHm9O+HHvMLb0McWpY/7DlzoP3sm8XZjk3DE3yfSYMOYAgB5in24AqSUUtFcSbt5q32eZV8IslGlOHfOWZnuV8u119j3c356fnDPcHXHqmJtkekwYcwDAPiLNl4RuAAAAAAC6KdJ8yeXlAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGOKY0H3HHXeopKRE/fr104ABAxJdDgAAAAAA++WY0N3a2qrzzz9fM2bMSHQpAAAAAABEJC3RBUSqoqJCkvTII48kthAAAAAAACLkmJluAAAAAACcxjEz3dHYtWuXdu3a1fY4EAgksBoAAAAAQG+T0Jnu2bNny+Vydfnx/vvvR93/nDlz5PP52j5yc3NjWD0AAAAAAF1zWZZlJerFt23bpk8//bTLNgUFBUpPT297/Mgjj+iaa67RZ599tt/+O5rpzs3Nld/vl9frjbpuAHCcUFDasEpq3ir1HyLllUhuT6Kr2j+TdTt1TExjXAAgNfDz3LhAICCfz7fffJnQy8sHDx6swYMHG+u/T58+6tOnj7H+AcARapZIlTdIgU++OuYdJpXeLRVOTlxd+2OybqeOiWmMCwCkBn6eJxXHLKS2ceNGrVmzRhs3blQwGNSaNWu0Zs0aNTc3J7o0AEheNUukJ6eF/6MrSYF6+3jNksTUtT8m63bqmJjGuABAauDnedJJ6OXl3XHppZfq0UcfbXf8pZde0sSJEyPqI9LpfwBICaGgdP+Y9v/otnHZf/W+5t3kutzMZN1OHRPTGBcASA38PI+rSPOlY2a6H3nkEVmW1e4j0sANAL3OhlVd/KMrSZYU2Gy3SyYm63bqmJjGuABAauDneVJyTOgGAHRT89bYtosXk3U7dUxMY1wAIDXw8zwpEboBIFX1HxLbdvFism6njolpjAsApAZ+niclQjcApKq8Evu+Lbk6aeCSvMPtdsnEZN1OHRPTGBcASA38PE9KhG4ASFVuj701iKT2//h++bj0ruRbSMVk3U4dE9MYFwBIDfw8T0qEbgBIZYWTpQsWSt6c8OPeYfbxZN2r02TdTh0T0xgXAEgN/DxPOo7ZMiwW2DIMQK8VCtorlTZvte/jyitxxl+5Tdbt1DExjXEBgNTAz3PjIs2XhG4AAAAAALop5fbpBgAAAADAaQjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCFpiS4AQJRCQWnDKql5q9R/iJRXIrk9ia4qMk6u3alMjrlT+0Zq2d0qvTlf2l4nDRwpjZ8upaUnuqrUxvcnAESE0A04Uc0SqfIGKfDJV8e8w6TSu6XCyYmrKxJOrt2pTI65U/tGall+s7T6t5IV2uvYTVLxLOmM2xJXVyrj+xMAIuayLMtKdBHxEggE5PP55Pf75fV6E10OEJ2aJdKT0yTt+63rsv9zwcLk/YXHybU7lckxd2rfSC3Lb5ZWPdD58yVXE7xjje9PAJAUeb7knm7ASUJBe2ah3S86+upY5Wy7XbJxcu1OZXLMndo3UsvuVnuGuyur59rtEBt8fwJAtxG6ASfZsCr8Ur52LCmw2W6XbJxcu1OZHHOn9o3U8ub88EvKO2IF7XaIDb4/AaDbCN2AkzRvjW27eHJy7U5lcsyd2jdSy/a62LbD/vH9CQDdRugGnKT/kNi2iycn1+5UJsfcqX0jtQwcGdt22D++PwGg2wjdgJPkldirw+5ZrKYdl+QdbrdLNk6u3alMjrlT+0ZqGT9dcu3nVxmXx26H2OD7EwC6jdANOInbY2/HIqn9LzxfPi69Kzn3SXVy7U5lcsyd2jdSS1q6vS1YV4pnsl93LPH9CQDdRugGnKZwsr0dizcn/Lh3WPJv0+Lk2p3K5Jg7tW+kljNus7cF23fG2+VhuzBT+P4EgG5hn27AqUJBe3XY5q32vXN5Jc6ZWXBy7U5lcsyd2jdSy+5We5Xy7XX2PdzjpzPDbRrfnwB6uUjzJaEbAAAAAIBuijRfcnk5AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYkpboAgBEKRSUNqySmrdK/YdIeSWS25PoqpCsdrdKb86XttdJA0dK46dLaemJrgoAACDlEboBJ6pZIlXeIAU++eqYd5hUerdUODlxdSE5Lb9ZWv1byQrtdewmqXiWdMZtiasLAACgF+DycsBpapZIT04LD9ySFKi3j9csSUxdSE7Lb5ZWPRAeuCX78aoH7OcBAABgDKEbcJJQ0J7hltXBk18eq5xttwN2t9oz3F1ZPdduBwAAACMI3YCTbFjVfoY7jCUFNtvtgDfnt5/h3pcVtNsBAADACEI34CTNW2PbDqlte11s2wEAAKDbCN2Ak/QfEtt2SG0DR8a2HQAAALqN0A04SV6JvUq5XJ00cEne4XY7YPx0ybWfH/Muj90OAAAARhC6ASdxe+xtwSS1D95fPi69i/26YUtLt7cF60rxTPbrBgAAMIjQDThN4WTpgoWSNyf8uHeYfZx9urG3M26TSq5uP+Pt8tjH2acbAADAKJdlWR3tPZSSAoGAfD6f/H6/vF5vossBeiYUtFcpb95q38OdV8IMNzq3u9VepXx7nX0P9/jpzHADAAD0QKT5Mi2ONQGIJbdHyj8p0VXAKdLS7UvJAQAAEFdcXg4AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIakJboApIhQUNqwSmreKvUfIuWVSG5PoqtCtHg/Uw/vKVId5zgAIEkRutFzNUukyhukwCdfHfMOk0rvlgonJ64uRIf3M/XwniLVcY4DAJIYl5ejZ2qWSE9OC/9FR5IC9fbxmiWJqQvR4f1MPbynSHWc4wCAJEfoRvRCQXtmQVYHT355rHK23Q7Jj/cz9fCeItVxjgMAHIDQjehtWNV+ZiGMJQU22+2Q/Hg/Uw/vKVId5zgAwAEI3Yhe89bYtkNi8X6mHt5TpDrOcQCAAxC6Eb3+Q2LbDonF+5l6eE+R6jjHAQAOQOhG9PJK7NVh5eqkgUvyDrfbIfnxfqYe3lOkOs5xAIADELoRPbfH3o5FUvtfeL58XHoX+6Q6Be9n6uE9RarjHAcAOAChGz1TOFm6YKHkzQk/7h1mH2d/VGfh/Uw9vKdIdZzjAIAk57Isq6N9NlJSIBCQz+eT3++X1+tNdDmpJRS0V4dt3mrfO5dXwsyCk/F+ph7eU6Q6znEAQJxFmi8J3QAAAAAAdFOk+ZLLywEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABiSlugCAABAN+1uld6cL22vkwaOlMZPl9LSE13V/oWC0oZVUvNWqf8QKa9EcnsSXRUAAEYRugEAcJLlN0urfytZob2O3SQVz5LOuC1xde1PzRKp8gYp8MlXx7zDpNK7pcLJiasLAADDuLwcAACnWH6ztOqB8MAt2Y9XPWA/n4xqlkhPTgsP3JIUqLeP1yxJTF0AAMSBI0J3XV2dLr/8cuXn56tv37465JBDVF5ertbW1kSXBgBAfOxutWe4u7J6rt0umYSC9gy3rA6e/PJY5Wy7HQAAKcgRofv9999XKBTSww8/rP/85z+677779NBDD+nnP/95oksDACA+3pzffoZ7X1bQbpdMNqxqP8MdxpICm+12AACkIEfc011aWqrS0tK2xwUFBfrggw80b948/epXv0pgZQAAxMn2uti2i5fmrbFtBwCAwzhiprsjfr9fWVlZiS4DAID4GDgytu3ipf+Q2LYDAMBhHBm6169frwcffFBXXHFFl+127dqlQCAQ9gEAgCONny659vPPtstjt0smeSX2KuVyddLAJXmH2+0AAEhBCQ3ds2fPlsvl6vLj/fffD/uczZs3q7S0VOeff76mT+/6F4s5c+bI5/O1feTm5pr8cgAAMCct3d4WrCvFM5Nvv263x94WTFL74P3l49K72K8bAJCyXJZldbScaFxs27ZNn376aZdtCgoKlJ5u/wLxySefaOLEiTrhhBP0yCOPyO3u+m8Gu3bt0q5du9oeBwIB5ebmyu/3y+v19vwLAAAg3jrap9vlsQO34/bpHm4HbvbpBgA4UCAQkM/n22++TGjo7o7NmzfrlFNO0XHHHafFixfL4+n+X8QjHRQAAJLa7lZ7lfLtdfY93OOnJ98Md0dCQXuV8uat9j3ceSXMcAMAHCulQvfmzZs1ceJE5eXl6dFHHw0L3EOHDo24H0I3AAAAACAWIs2Xjtgy7IUXXtD69eu1fv16jRgxIuw5B/zNAAAAAADQSzli9fJLL71UlmV1+AEAAAAAQLJyROgGAAAAAMCJCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGBIWqILAJCEQkFpwyqpeavUf4iUVyK5PYmuCgAAAHAcQjeAcDVLpMobpMAnXx3zDpNK75YKJyeuLgAAAMCBuLwcwFdqlkhPTgsP3JIUqLeP1yxJTF0AAACAQxG6AdhCQXuGW1YHT355rHK23Q4AAABARAjdAGwbVrWf4Q5jSYHNdjsAAAAAESF0A7A1b41tOwAAAACEbgBf6j8ktu0AAAAAELoBfCmvxF6lXK5OGrgk73C7HQAAAICIELoB2Nwee1swSe2D95ePS+9iv24AAACgGwjdAL5SOFm6YKHkzQk/7h1mH2efbgAAAKBb0hJdAIAkUzhZOuJse5Xy5q32Pdx5JcxwAwAAAFEgdANoz+2R8k9KdBUAAACA43F5OQAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQjcAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAEEI3AAAAAACGELoBAAAAADCE0A0AAAAAgCGEbgAAAAAADCF0AwAAAABgCKEbAAAAAABDCN0AAAAAABhC6AYAAAAAwJC0RBcQT5ZlSZICgUCCKwEAAAAAONmeXLknZ3amV4XupqYmSVJubm6CKwEAAAAApIKmpib5fL5On3dZ+4vlKSQUCumTTz5RZmamXC5XVH0EAgHl5uZq06ZN8nq9Ma4QSG6c/+itOPfRm3H+ozfj/EdXLMtSU1OThg0bJre78zu3e9VMt9vt1ogRI2LSl9fr5RsPvRbnP3orzn30Zpz/6M04/9GZrma492AhNQAAAAAADCF0AwAAAABgCKG7m/r06aPy8nL16dMn0aUAccf5j96Kcx+9Gec/ejPOf8RCr1pIDQAAAACAeGKmGwAAAAAAQwjdAAAAAAAYQugGAAAAAMAQQneU6urqdPnllys/P199+/bVIYccovLycrW2tia6NCAu7rjjDpWUlKhfv34aMGBAossBjJo7d65GjhypjIwMHX/88aqqqkp0SYBxK1euVFlZmYYNGyaXy6Vnn3020SUBcTFnzhyNHz9emZmZys7O1jnnnKMPPvgg0WXBwQjdUXr//fcVCoX08MMP6z//+Y/uu+8+PfTQQ/r5z3+e6NKAuGhtbdX555+vGTNmJLoUwKg///nPuvbaa1VeXq7q6modffTROvPMM9XQ0JDo0gCjPv/8cx199NGaO3duoksB4urll1/WzJkz9frrr+uFF17QF198oTPOOEOff/55okuDQ7F6eQzdc889mjdvnj766KNElwLEzSOPPKJrrrlGn332WaJLAYw4/vjjNX78eP32t7+VJIVCIeXm5uqqq67S7NmzE1wdEB8ul0vPPPOMzjnnnESXAsTdtm3blJ2drZdfflknn3xyosuBAzHTHUN+v19ZWVmJLgMAECOtra16++23dfrpp7cdc7vdOv3007V69eoEVgYAiBe/3y9J/J6PqBG6Y2T9+vV68MEHdcUVVyS6FABAjPzvf/9TMBjUkCFDwo4PGTJEW7ZsSVBVAIB4CYVCuuaaa3TiiSdqzJgxiS4HDkXo3sfs2bPlcrm6/Hj//ffDPmfz5s0qLS3V+eefr+nTpyeocqDnojn/AQAAUtXMmTO1du1aPfHEE4kuBQ6WlugCks11112nSy+9tMs2BQUFbf//ySef6JRTTlFJSYl+//vfG64OMKu75z+Q6g466CB5PB5t3bo17PjWrVs1dOjQBFUFAIiHWbNm6W9/+5tWrlypESNGJLocOBihex+DBw/W4MGDI2q7efNmnXLKKTruuOO0YMECud1cOABn6875D/QG6enpOu644/Tiiy+2LSAVCoX04osvatasWYktDgBghGVZuuqqq/TMM89oxYoVys/PT3RJcDhCd5Q2b96siRMnKi8vT7/61a+0bdu2tueY/UBvsHHjRjU2Nmrjxo0KBoNas2aNJOnQQw9V//79E1scEEPXXnutLrnkEo0bN05FRUW6//779fnnn+uyyy5LdGmAUc3NzVq/fn3b49raWq1Zs0ZZWVk6+OCDE1gZYNbMmTP12GOP6a9//asyMzPb1vDw+Xzq27dvgquDE7FlWJQeeeSRTn/hYkjRG1x66aV69NFH2x1/6aWXNHHixPgXBBj029/+Vvfcc4+2bNmir33ta3rggQd0/PHHJ7oswKgVK1bolFNOaXf8kksu0SOPPBL/goA4cblcHR5fsGDBfm/DAzpC6AYAAAAAwBBuQgYAAAAAwBBCNwAAAAAAhhC6AQAAAAAwhNANAAAAAIAhhG4AAAAAAAwhdAMAAAAAYAihGwAAAAAAQwjdAAAAAAAYQugGAKAXufTSS3XOOedE1HbixIm65pprjNYTqRUrVsjlcumzzz5LdCkAAHQLoRsAACSVZAr7AAD0FKEbAAAAAABDCN0AAMTRU089paOOOkp9+/bVoEGDdPrpp+vzzz+XJP3hD3/Q6NGjlZGRoSOOOEK/+93v2j6vrq5OLpdLTzzxhEpKSpSRkaExY8bo5ZdfbmsTDAZ1+eWXKz8/X3379tXhhx+u3/zmNzGrfdeuXbr++us1fPhwHXjggTr++OO1YsWKtucfeeQRDRgwQM8//7xGjx6t/v37q7S0VPX19W1tdu/erauvvloDBgzQoEGDdMMNN+iSSy5pu+T90ksv1csvv6zf/OY3crlccrlcqqura/v8t99+W+PGjVO/fv1UUlKiDz74IGZfHwAAJhC6AQCIk/r6el144YX6/ve/r/fee08rVqzQueeeK8uy9Kc//Um33HKL7rjjDr333nu68847dfPNN+vRRx8N6+OnP/2prrvuOr3zzjsqLi5WWVmZPv30U0lSKBTSiBEj9P/+3/9TTU2NbrnlFv385z/Xk08+GZP6Z82apdWrV+uJJ57Qv//9b51//vkqLS3VunXr2trs2LFDv/rVr7Ro0SKtXLlSGzdu1PXXX9/2/N13360//elPWrBggV577TUFAgE9++yzbc//5je/UXFxsaZPn676+nrV19crNze37flf/OIXuvfee/XWW28pLS1N3//+92PytQEAYIwFAADi4u2337YkWXV1de2eO+SQQ6zHHnss7Nhtt91mFRcXW5ZlWbW1tZYk66677mp7/osvvrBGjBhh3X333Z2+5syZM63zzjuv7fEll1xifetb34qo3gkTJlg//vGPLcuyrA0bNlgej8favHlzWJvTTjvNuvHGGy3LsqwFCxZYkqz169e3PT937lxryJAhbY+HDBli3XPPPW2Pd+/ebR188MFhNe39unu89NJLliTrH//4R9ux5557zpJk7dy5M6KvBwCAREhLaOIHAKAXOfroo3XaaafpqKOO0plnnqkzzjhD3/nOd5Senq4PP/xQl19+uaZPn97Wfvfu3fL5fGF9FBcXt/1/Wlqaxo0bp/fee6/t2Ny5c/XHP/5RGzdu1M6dO9Xa2qqvfe1rPa793XffVTAY1GGHHRZ2fNeuXRo0aFDb4379+umQQw5pe5yTk6OGhgZJkt/v19atW1VUVNT2vMfj0XHHHadQKBRRHWPHjg3rW5IaGhp08MEHd/+LAgAgDgjdAADEicfj0QsvvKBVq1Zp+fLlevDBB/WLX/xCS5culSTNnz9fxx9/fLvPidQTTzyh66+/Xvfee6+Ki4uVmZmpe+65R2+88UaPa29ubpbH49Hbb7/drqb+/fu3/f8BBxwQ9pzL5ZJlWT1+/Y76d7lckhRxYAcAIBEI3QAAxJHL5dKJJ56oE088Ubfccovy8vL02muvadiwYfroo4908cUXd/n5r7/+uk4++WRJ9kz422+/rVmzZkmSXnvtNZWUlOjKK69sa//hhx/GpO5jjjlGwWBQDQ0NOumkk6Lqw+fzaciQIXrzzTfbvoZgMKjq6uqw2fj09HQFg8FYlA0AQMIRugEAiJM33nhDL774os444wxlZ2frjTfe0LZt2zR69GhVVFTo6quvls/nU2lpqXbt2qW33npL27dv17XXXtvWx9y5czVq1CiNHj1a9913n7Zv3962mNioUaO0cOFCPf/888rPz9eiRYv05ptvKj8/v8e1H3bYYbr44os1bdo03XvvvTrmmGO0bds2vfjiixo7dqzOPvvsiPq56qqrNGfOHB166KE64ogj9OCDD2r79u1ts9aSNHLkSL3xxhuqq6tT//79lZWV1eP6AQBIFEI3AABx4vV6tXLlSt1///0KBALKy8vTvffeq2984xuS7Puh77nnHv30pz/VgQceqKOOOkrXXHNNWB933XWX7rrrLq1Zs0aHHnqolixZooMOOkiSdMUVV+idd97Rd7/7XblcLl144YW68sor9fe//z0m9S9YsEC33367rrvuOm3evFkHHXSQTjjhBH3zm9+MuI8bbrhBW7Zs0bRp0+TxePTDH/5QZ555Ztgl69dff70uueQSFRYWaufOnaqtrY1J/QAAJILLiuWNVgAAwIi6ujrl5+frnXfeicnCaMkiFApp9OjRuuCCC3TbbbcluhwAAGKOmW4AABA3GzZs0PLlyzVhwgTt2rVLv/3tb1VbW6uLLroo0aUBAGCEO9EFAACA+Nu4caP69+/f6cfGjRuNvK7b7dYjjzyi8ePH68QTT9S7776rf/zjHxo9erSR1wMAING4vBwAgF5o9+7dqqur6/T5kSNHKi2NC+IAAOgpQjcAAAAAAIZweTkAAAAAAIYQugEAAAAAMITQDQAAAACAIYRuAAAAAAAMIXQDAAAAAGAIoRsAAAAAAEMI3QAAAAAAGELoBgAAAADAkP8P3wPl9IW76C0AAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import requests\n",
     "import json\n",
@@ -177,26 +153,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 271,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89a63771066248d5ab936c505ad551b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
+       "interactive(children=(IntSlider(value=20, description='elev', max=90, step=5), IntSlider(value=30, description…"
       ]
      },
+     "execution_count": 7,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "import requests\n",
     "import json\n",
     "import matplotlib.pyplot as plt\n",
+    "import ipywidgets as widgets\n",
     "from mpl_toolkits.mplot3d import Axes3D\n",
+    "from IPython.display import display\n",
     "\n",
+    "#region setup\n",
     "# Endpunkt für 3D KMeans Route \n",
     "url = \"http://localhost:8080/basic/perform-3d-kmeans/\"\n",
     "\n",
@@ -207,9 +191,10 @@
     "    \"Column 3\": 2,\n",
     "    \"distance_metric\": \"EUCLIDEAN\",\n",
     "    \"kmeans_type\": \"OptimizedKMeans\",\n",
-    "    \"k_clusters\": 3,\n",
+    "    \"k_clusters\": 5,\n",
     "    \"user_id\": 0,\n",
-    "    \"request_id\": 0\n",
+    "    \"request_id\": 0,\n",
+    "    \"normalize\": True\n",
     "}\n",
     "\n",
     "files = {\n",
@@ -221,44 +206,43 @@
     "\n",
     "# Lade JSON-Antwort in Variable\n",
     "result = response.json()\n",
+    "#endregion\n",
     "\n",
-    "# Erstelle 3D-Plot\n",
-    "fig = plt.figure(figsize=(10, 10))  # Erhöhte Grafikgröße\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
+    "#region interactive plot\n",
     "\n",
-    "# Gehe durch jeden Cluster\n",
-    "for cluster in result[\"cluster\"]:\n",
-    "    centroid = cluster[\"centroid\"]\n",
-    "    points = cluster[\"points\"]\n",
+    "def plot_3d(elev=20, azim=30):\n",
+    "    fig = plt.figure(figsize=(10, 10))\n",
+    "    ax = fig.add_subplot(111, projection='3d')\n",
     "\n",
-    "    # Extrahiere Datenpunkte\n",
-    "    point_x = [p[\"x\"] for p in points]\n",
-    "    point_y = [p[\"y\"] for p in points]\n",
-    "    point_z = [p[\"z\"] for p in points]\n",
+    "    for cluster in result[\"cluster\"]:\n",
+    "        centroid = cluster[\"centroid\"]\n",
+    "        points = cluster[\"points\"]\n",
     "\n",
-    "    # Plotte Cluster\n",
-    "    ax.scatter(point_x, point_y, point_z, s=50, label=f\"Cluster {cluster['clusterNr']}\")\n",
+    "        # Extrahiere Datenpunkte\n",
+    "        point_x = [p[\"x\"] for p in points]\n",
+    "        point_y = [p[\"y\"] for p in points]\n",
+    "        point_z = [p[\"z\"] for p in points]\n",
     "\n",
-    "    # Plotte Zentroid\n",
-    "    ax.scatter(centroid[\"x\"], centroid[\"y\"], centroid[\"z\"], color=\"black\", marker=\"x\", s=100)\n",
+    "        # Plotte Cluster\n",
+    "        ax.scatter(point_x, point_y, point_z, s=50, label=f\"Cluster {cluster['clusterNr']}\")\n",
     "\n",
-    "# Achsenbeschriftung mit erhöhtem Abstand (labelpad)\n",
-    "ax.set_xlabel(result[\"x_label\"], labelpad=20)\n",
-    "ax.set_ylabel(result[\"y_label\"], labelpad=20)\n",
-    "ax.set_zlabel(\"Z Label\", labelpad=20)  # Da es kein z_label gibt, verwenden Sie einen Platzhalter. \n",
+    "        # Plotte Zentroid\n",
+    "        ax.scatter(centroid[\"x\"], centroid[\"y\"], centroid[\"z\"], color=\"black\", marker=\"x\", s=100)\n",
     "\n",
-    "# Verbesserte Grafikoptionen\n",
-    "ax.grid(True)  # Gitterlinien hinzufügen\n",
-    "ax.view_init(45, 45)  # Ansichtswinkel anpassen\n",
+    "    ax.set_xlabel(f\"X: {result['x_label']}\", labelpad=20)\n",
+    "    ax.set_ylabel(f\"Y: {result['y_label']}\", labelpad=20)\n",
+    "    ax.set_zlabel(f\"Z: {result['z_label']}\", labelpad=20)\n",
+    "    ax.grid(True)\n",
+    "    ax.view_init(elev=elev, azim=azim)\n",
     "\n",
-    "# Platzierung von Titel und Legende anpassen\n",
-    "ax.legend(loc='upper left')\n",
-    "ax.set_title(f\"3D KMeans mit k={len(result['cluster'])}\", pad=30)  # Erhöhter Abstand des Titels\n",
+    "    ax.legend(loc='upper left')\n",
+    "    ax.set_title(f\"3D KMeans mit k={len(result['cluster'])}\", pad=30)\n",
     "\n",
-    "# Optimiert das Layout\n",
-    "plt.tight_layout()\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "widgets.interactive(plot_3d, elev=(0, 90, 5), azim=(0, 360, 10))\n",
     "\n",
-    "plt.show()\n"
+    "#endregion\n"
    ]
   },
   {
@@ -270,28 +254,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 272,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1f0e33be08346dd9a9644ca839d1df9",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
+       "interactive(children=(IntSlider(value=20, description='elev', max=90, step=5), IntSlider(value=30, description…"
       ]
      },
+     "execution_count": 6,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "import requests\n",
-    "import json\n",
     "import matplotlib.pyplot as plt\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "\n",
+    "import ipywidgets as widgets\n",
     "# Endpunkt für 3D KMeans Route \n",
-    "url = \"http://localhost:8080/basic/perform-3d-kmeans/\"\n",
+    "url = \"http://localhost:8080/advanced/perform-advanced-3d-kmeans/\"\n",
     "\n",
     "# Daten für die Anfrage\n",
     "params = {\n",
@@ -301,7 +288,8 @@
     "    \"distance_metric\": \"EUCLIDEAN\",\n",
     "    \"kmeans_type\": \"OptimizedKMeans\",\n",
     "    \"user_id\": 0,\n",
-    "    \"request_id\": 0\n",
+    "    \"request_id\": 0,\n",
+    "    \"normalize\": True\n",
     "}\n",
     "\n",
     "files = {\n",
@@ -314,40 +302,38 @@
     "# Lade JSON-Antwort in Variable\n",
     "result = response.json()\n",
     "\n",
-    "# Erstelle 3D-Plot\n",
-    "fig = plt.figure(figsize=(10, 10))  \n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
+    "def plot_3d(elev=20, azim=30):\n",
+    "    fig = plt.figure(figsize=(10, 10))\n",
+    "    ax = fig.add_subplot(111, projection='3d')\n",
     "\n",
-    "# Gehe durch jeden Cluster\n",
-    "for cluster in result[\"cluster\"]:\n",
-    "    centroid = cluster[\"centroid\"]\n",
-    "    points = cluster[\"points\"]\n",
+    "    for cluster in result[\"cluster\"]:\n",
+    "        centroid = cluster[\"centroid\"]\n",
+    "        points = cluster[\"points\"]\n",
     "\n",
-    "    # Extrahiere Datenpunkte\n",
-    "    point_x = [p[\"x\"] for p in points]\n",
-    "    point_y = [p[\"y\"] for p in points]\n",
-    "    point_z = [p[\"z\"] for p in points]\n",
+    "        # Extrahiere Datenpunkte\n",
+    "        point_x = [p[\"x\"] for p in points]\n",
+    "        point_y = [p[\"y\"] for p in points]\n",
+    "        point_z = [p[\"z\"] for p in points]\n",
     "\n",
-    "    # Plotte Cluster\n",
-    "    ax.scatter(point_x, point_y, point_z, s=50, label=f\"Cluster {cluster['clusterNr']}\")\n",
+    "        # Plotte Cluster\n",
+    "        ax.scatter(point_x, point_y, point_z, s=50, label=f\"Cluster {cluster['clusterNr']}\")\n",
     "\n",
-    "    # Plotte Zentroid\n",
-    "    ax.scatter(centroid[\"x\"], centroid[\"y\"], centroid[\"z\"], color=\"black\", marker=\"x\", s=100)\n",
+    "        # Plotte Zentroid\n",
+    "        ax.scatter(centroid[\"x\"], centroid[\"y\"], centroid[\"z\"], color=\"black\", marker=\"x\", s=100)\n",
     "\n",
-    "# Achsenbeschriftung\n",
-    "ax.set_xlabel(result[\"x_label\"], labelpad=20)\n",
-    "ax.set_ylabel(result[\"y_label\"], labelpad=20)\n",
-    "ax.set_zlabel(\"Z Label\", labelpad=20)  # Platzhalter für das z-Label\n",
+    "    ax.set_xlabel(f\"X: {result['x_label']}\", labelpad=20)\n",
+    "    ax.set_ylabel(f\"Y: {result['y_label']}\", labelpad=20)\n",
+    "    ax.set_zlabel(f\"Z: {result['z_label']}\", labelpad=20)\n",
+    "    ax.grid(True)\n",
+    "    ax.view_init(elev=elev, azim=azim)\n",
     "\n",
-    "# Grafikoptionen\n",
-    "ax.grid(True)  \n",
-    "ax.view_init(45, 45)  \n",
+    "    ax.legend(loc='upper left')\n",
+    "    ax.set_title(f\"3D KMeans mit k={len(result['cluster'])}\", pad=30)\n",
     "\n",
-    "ax.legend(loc='upper left')\n",
-    "ax.set_title(f\"3D KMeans mit k={len(result['cluster'])}\", pad=30)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
     "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
+    "widgets.interactive(plot_3d, elev=(0, 90, 5), azim=(0, 360, 10))\n"
    ]
   },
   {
@@ -359,79 +345,129 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 273,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
+       "<Figure size 2000x1000 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dateiname: IRIS.csv\n",
-      "Verwendete Distanzmetrik: EUCLIDEAN\n",
-      "Anzahl der Iterationen: 1\n",
-      "Wert von k: 4\n",
-      "Wichtige Features:\n",
-      "- sepal_length: 58.54%\n",
-      "- sepal_width: 27.12%\n"
-     ]
     }
    ],
    "source": [
     "import requests\n",
     "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D  # für 3D-Plot\n",
     "\n",
-    "# Daten an den Endpunkt senden\n",
     "url = \"http://localhost:8080/basic/perform-nd-kmeans/\"\n",
-    "params = {\n",
-    "    \"k_clusters\":4\n",
-    "}\n",
     "files = {\n",
-    "    \"file\": open(\"IRIS.csv\", \"rb\")\n",
+    "    \"file\": open(\"drug200.csv\", \"rb\")\n",
     "}\n",
-    "response = requests.post(url, files=files, params=params)\n",
-    "result = response.json()\n",
     "\n",
-    "# Ergebnisdaten extrahieren\n",
-    "clusters = result[\"clusters\"]\n",
-    "x_label = result[\"x_label\"]\n",
-    "y_label = result[\"y_label\"]\n",
+    "# 2D-Anfrage\n",
+    "params_2d = {\n",
+    "    \"use_3d_model\": False,\n",
+    "    \"k_clusters\": 5\n",
+    "}\n",
+    "response_2d = requests.post(url, files=files, params=params_2d)\n",
+    "result_2d = response_2d.json()\n",
     "\n",
     "# 2D-Visualisierung\n",
-    "plt.figure(figsize=(10, 10))\n",
-    "for cluster in clusters:\n",
+    "plt.figure(figsize=(20, 10))\n",
+    "\n",
+    "# 2D-Plot\n",
+    "plt.subplot(1, 2, 1)  # 1 Zeile, 2 Spalten, aktuelle Position ist 1\n",
+    "clusters_2d = result_2d[\"cluster\"]\n",
+    "x_label_2d = result_2d[\"x_label\"]\n",
+    "y_label_2d = result_2d[\"y_label\"]\n",
+    "for cluster in clusters_2d:\n",
     "    points = cluster[\"points\"]\n",
-    "    x_values = [point[\"x\"] for point in points]\n",
-    "    y_values = [point[\"y\"] for point in points]\n",
+    "    x_values = [point['x'] for point in points]  # Changed here\n",
+    "    y_values = [point['y'] for point in points]  # Changed here\n",
     "    plt.scatter(x_values, y_values, label=f\"Cluster {cluster['clusterNr']}\")\n",
-    "\n",
-    "    # Zentroid darstellen\n",
     "    centroid = cluster[\"centroid\"]\n",
     "    plt.scatter(centroid[\"x\"], centroid[\"y\"], color=\"black\", marker=\"x\", s=100)\n",
-    "\n",
-    "plt.xlabel(x_label)\n",
-    "plt.ylabel(y_label)\n",
+    "plt.xlabel(x_label_2d)\n",
+    "plt.ylabel(y_label_2d)\n",
     "plt.legend()\n",
-    "plt.title(f\"2D representation of n-dimensional KMeans with k={result['k_value']}\")\n",
+    "plt.title(f\"2D representation of n-dimensional KMeans with k={result_2d['k_value']}\")\n",
+    "\n",
     "plt.show()\n",
     "\n",
-    "# Weitere Informationen ausgeben\n",
-    "print(\"Dateiname:\", result[\"name\"])\n",
-    "print(\"Verwendete Distanzmetrik:\", result[\"used_distance_metric\"])\n",
-    "print(\"Anzahl der Iterationen:\", result[\"iterations\"])\n",
-    "print(\"Wert von k:\", result[\"k_value\"])\n",
-    "print(\"Wichtige Features:\")\n",
-    "for feature, importance in result[\"important_features\"].items():\n",
-    "    print(f\"- {feature}: {importance:.2%}\")\n"
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1ecde6008bb40f1ad045b513f98f218",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=20, description='elev', max=90, step=5), IntSlider(value=30, description…"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import matplotlib.pyplot as plt\n",
+    "import ipywidgets as widgets\n",
+    "\n",
+    "files = {\n",
+    "    \"file\": open(\"drug200.csv\", \"rb\")\n",
+    "}\n",
+    "\n",
+    "url = \"http://localhost:8080/basic/perform-nd-kmeans/\"\n",
+    "\n",
+    "# 3D-Anfrage\n",
+    "params_3d = {\n",
+    "    \"k_clusters\": 5,\n",
+    "    \"use_3d_model\": True\n",
+    "}\n",
+    "response_3d = requests.post(url, files=files, params=params_3d)\n",
+    "result_3d = response_3d.json()\n",
+    "\n",
+    "def plot_3d_clusters(elev=20, azim=30):\n",
+    "    plt.figure(figsize=(25, 12))\n",
+    "\n",
+    "    # 3D-Plot\n",
+    "    ax = plt.subplot(1, 2, 2, projection='3d')\n",
+    "    clusters_3d = result_3d[\"cluster\"]\n",
+    "    x_label_3d = result_3d[\"x_label\"]\n",
+    "    y_label_3d = result_3d[\"y_label\"]\n",
+    "    z_label_3d = result_3d[\"z_label\"]\n",
+    "    for cluster in clusters_3d:\n",
+    "        points = cluster[\"points\"]\n",
+    "        x_values = [point['x'] for point in points]\n",
+    "        y_values = [point['y'] for point in points]\n",
+    "        z_values = [point['z'] for point in points]\n",
+    "        ax.scatter(x_values, y_values, z_values, label=f\"Cluster {cluster['clusterNr']}\")\n",
+    "        centroid = cluster[\"centroid\"]\n",
+    "        ax.scatter(centroid[\"x\"], centroid[\"y\"], centroid[\"z\"], color=\"black\", marker=\"x\", s=100)\n",
+    "    ax.set_xlabel(x_label_3d)\n",
+    "    ax.set_ylabel(y_label_3d)\n",
+    "    ax.set_zlabel(z_label_3d)\n",
+    "    ax.view_init(elev=elev, azim=azim)\n",
+    "    ax.legend()\n",
+    "    ax.set_title(f\"3D representation of n-dimensional KMeans with k={result_3d['k_value']}\")\n",
+    "    plt.show()\n",
+    "\n",
+    "widgets.interactive(plot_3d_clusters, elev=(0, 90, 5), azim=(0, 360, 10))\n"
    ]
   },
   {
@@ -443,79 +479,130 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 274,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
+       "<Figure size 2000x1000 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dateiname: IRIS.csv\n",
-      "Verwendete Distanzmetrik: EUCLIDEAN\n",
-      "Anzahl der Iterationen: 0\n",
-      "Wert von k: 3\n",
-      "Wichtige Features:\n",
-      "- sepal_length: 58.54%\n",
-      "- sepal_width: 27.12%\n"
-     ]
     }
    ],
    "source": [
     "import requests\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "# Daten an den Endpunkt senden\n",
     "url = \"http://localhost:8080/advanced/perform-advanced-nd-kmeans/\"\n",
-    "params = {\n",
-    "\n",
-    "}\n",
     "files = {\n",
-    "    \"file\": open(\"IRIS.csv\", \"rb\")\n",
+    "    \"file\": open(\"drug200.csv\", \"rb\")\n",
     "}\n",
-    "response = requests.post(url, files=files, params=params)\n",
-    "result = response.json()\n",
     "\n",
-    "# Ergebnisdaten extrahieren\n",
-    "clusters = result[\"clusters\"]\n",
-    "x_label = result[\"x_label\"]\n",
-    "y_label = result[\"y_label\"]\n",
+    "# 2D-Anfrage\n",
+    "params_2d = {\n",
+    "    \"use_3d_model\": False\n",
+    "}\n",
+    "response_2d = requests.post(url, files=files, params=params_2d)\n",
+    "result_2d = response_2d.json()\n",
     "\n",
     "# 2D-Visualisierung\n",
-    "plt.figure(figsize=(10, 10))\n",
-    "for cluster in clusters:\n",
+    "plt.figure(figsize=(20, 10))\n",
+    "\n",
+    "# 2D-Plot\n",
+    "plt.subplot(1, 2, 1)  # 1 Zeile, 2 Spalten, aktuelle Position ist 1\n",
+    "clusters_2d = result_2d[\"cluster\"]\n",
+    "x_label_2d = result_2d[\"x_label\"]\n",
+    "y_label_2d = result_2d[\"y_label\"]\n",
+    "for cluster in clusters_2d:\n",
     "    points = cluster[\"points\"]\n",
-    "    x_values = [point[\"x\"] for point in points]\n",
-    "    y_values = [point[\"y\"] for point in points]\n",
+    "    x_values = [point['x'] for point in points]  # Changed here\n",
+    "    y_values = [point['y'] for point in points]  # Changed here\n",
     "    plt.scatter(x_values, y_values, label=f\"Cluster {cluster['clusterNr']}\")\n",
-    "\n",
-    "    # Zentroid darstellen\n",
     "    centroid = cluster[\"centroid\"]\n",
     "    plt.scatter(centroid[\"x\"], centroid[\"y\"], color=\"black\", marker=\"x\", s=100)\n",
-    "\n",
-    "plt.xlabel(x_label)\n",
-    "plt.ylabel(y_label)\n",
+    "plt.xlabel(x_label_2d)\n",
+    "plt.ylabel(y_label_2d)\n",
     "plt.legend()\n",
-    "plt.title(f\"2D representation of n-dimensional KMeans with k={result['k_value']}\")\n",
-    "plt.show()\n",
+    "plt.title(f\"2D representation of n-dimensional KMeans with k={result_2d['k_value']}\")\n",
     "\n",
-    "# Weitere Informationen ausgeben\n",
-    "print(\"Dateiname:\", result[\"name\"])\n",
-    "print(\"Verwendete Distanzmetrik:\", result[\"used_distance_metric\"])\n",
-    "print(\"Anzahl der Iterationen:\", result[\"iterations\"])\n",
-    "print(\"Wert von k:\", result[\"k_value\"])\n",
-    "print(\"Wichtige Features:\")\n",
-    "for feature, importance in result[\"important_features\"].items():\n",
-    "    print(f\"- {feature}: {importance:.2%}\")\n"
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f22c5fb34c3c43fe8db312ece4e900f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=20, description='elev', max=90, step=5), IntSlider(value=30, description…"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import matplotlib.pyplot as plt\n",
+    "import ipywidgets as widgets\n",
+    "\n",
+    "files = {\n",
+    "    \"file\": open(\"drug200.csv\", \"rb\")\n",
+    "}\n",
+    "\n",
+    "url = \"http://localhost:8080/advanced/perform-advanced-nd-kmeans/\"\n",
+    "\n",
+    "# 3D-Anfrage\n",
+    "params_3d = {\n",
+    "    \"distance_metric\": \"EUCLIDEAN\",\n",
+    "    \"kmeans_type\": \"OptimizedKMeans\",\n",
+    "    \"use_3d_model\": True\n",
+    "}\n",
+    "response_3d = requests.post(url, files=files, params=params_3d)\n",
+    "result_3d = response_3d.json()\n",
+    "\n",
+    "def plot_3d_clusters(elev=20, azim=30):\n",
+    "    fig = plt.figure(figsize=(25, 12))\n",
+    "\n",
+    "    # 3D-Plot\n",
+    "    ax = fig.add_subplot(1, 2, 2, projection='3d')\n",
+    "    clusters_3d = result_3d[\"cluster\"]\n",
+    "    x_label_3d = result_3d[\"x_label\"]\n",
+    "    y_label_3d = result_3d[\"y_label\"]\n",
+    "    z_label_3d = result_3d[\"z_label\"]\n",
+    "    \n",
+    "    for cluster in clusters_3d:\n",
+    "        points = cluster[\"points\"]\n",
+    "        x_values = [point['x'] for point in points]\n",
+    "        y_values = [point['y'] for point in points]\n",
+    "        z_values = [point['z'] for point in points]\n",
+    "        ax.scatter(x_values, y_values, z_values, label=f\"Cluster {cluster['clusterNr']}\")\n",
+    "        centroid = cluster[\"centroid\"]\n",
+    "        ax.scatter(centroid[\"x\"], centroid[\"y\"], centroid[\"z\"], color=\"black\", marker=\"x\", s=100)\n",
+    "        \n",
+    "    ax.set_xlabel(x_label_3d)\n",
+    "    ax.set_ylabel(y_label_3d)\n",
+    "    ax.set_zlabel(z_label_3d)\n",
+    "    ax.view_init(elev=elev, azim=azim)\n",
+    "    ax.legend()\n",
+    "    ax.set_title(f\"3D representation of n-dimensional KMeans with k={result_3d['k_value']}\")\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "widgets.interactive(plot_3d_clusters, elev=(0, 90, 5), azim=(0, 360, 10))\n"
    ]
   },
   {
@@ -527,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 275,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -588,6 +675,82 @@
     "    plt.tight_layout()\n",
     "    plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dessicion Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import graphviz\n",
+    "import json\n",
+    "from IPython.display import display\n",
+    "\n",
+    "# Aktualisierte URL\n",
+    "url = url = \"http://localhost:8080/classification_decision_tree/perform-classification-decision-tree/\"\n",
+    "\n",
+    "params = {\n",
+    "    \"SampleCount4Split\": 2,\n",
+    "    \"max_depth\": 100,\n",
+    "    \"SplitStrategy\": \"Best Split\",\n",
+    "    \"BestSplitStrategy\": \"Information Gain\",\n",
+    "    #\"ClassColumnNumber\": 5,\n",
+    "    \"ClassColumnName\": \"quality\",\n",
+    "    \"Pruning?\": True,\n",
+    "}\n",
+    "\n",
+    "# Datei hochladen\n",
+    "files = {\n",
+    "    #\"file\": open(\"drug200.csv\", \"rb\")\n",
+    "    \"file\": open(\"winequality-white.csv\", \"rb\")\n",
+    "}\n",
+    "response = requests.post(url=url, files=files, params=params)\n",
+    "result = response.json()\n",
+    "#print(json.dumps(result, indent=4, sort_keys=True))\n",
+    "\n",
+    "# Funktion zur Visualisierung des Entscheidungsbaums\n",
+    "def visualize_decision_tree(result):\n",
+    "    def visualize_node(dot, node, parent=None):\n",
+    "        if \"feature_name\" in node:\n",
+    "            feature_name = node[\"feature_name\"]\n",
+    "            feature_id_name = node[\"feature_id_name\"]\n",
+    "            treshold = str(node[\"treshold\"])\n",
+    "            dot.node(str(id(node)), f\"{feature_name}\\n{feature_id_name}\\nTreshold: {treshold}\")\n",
+    "        else:  # Dieser Teil wurde hinzugefügt, um Knoten ohne \"feature_name\" zu handhaben\n",
+    "            value = str(node.get(\"value\", \"Unknown\"))  # Verwenden Sie \"Unknown\", wenn kein Wert vorhanden ist\n",
+    "            dot.node(str(id(node)), f\"Class: {value}\")\n",
+    "\n",
+    "        if parent is not None:\n",
+    "            dot.edge(str(id(parent)), str(id(node)))\n",
+    "\n",
+    "        if \"left\" in node:\n",
+    "            visualize_node(dot, node[\"left\"], parent=node)\n",
+    "\n",
+    "        if \"right\" in node:\n",
+    "            visualize_node(dot, node[\"right\"], parent=node)\n",
+    "\n",
+    "    dot = graphviz.Digraph(format=\"png\")\n",
+    "    visualize_node(dot, result[\"root\"])\n",
+    "    dot.render(\"decision_tree\")\n",
+    "    display(dot)\n",
+    "\n",
+    "visualize_decision_tree(result=result)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Decision Tree](decision_tree.png)"
+   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/winequality-white.csv b/notebooks/winequality-white.csv
new file mode 100644
index 0000000..df5cbcb
--- /dev/null
+++ b/notebooks/winequality-white.csv
@@ -0,0 +1,4899 @@
+"fixed acidity","volatile acidity","citric acid","residual sugar","chlorides","free sulfur dioxide","total sulfur dioxide","density","pH","sulphates","alcohol","quality"
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