diff --git a/test/test/conftest.py b/test/test/conftest.py
index 57f38391..a811663c 100644
--- a/test/test/conftest.py
+++ b/test/test/conftest.py
@@ -4,7 +4,7 @@
 import numpy as np
 import pandas as pd
 import pytest
-from numpy.testing import assert_array_almost_equal, assert_array_equal
+from numpy.testing import assert_allclose, assert_array_equal
 from sklearn import datasets
 from sklearn.model_selection import BaseCrossValidator, GridSearchCV, KFold
 from sklearn.utils import Bunch
@@ -71,7 +71,7 @@ def n_jobs() -> int:
 @pytest.fixture
 def cv_kfold() -> KFold:
     # define a CV
-    return KFold(n_splits=K_FOLDS)
+    return KFold(n_splits=K_FOLDS, shuffle=True, random_state=42)
 
 
 @pytest.fixture
@@ -357,14 +357,14 @@ def check_ranking(
         only required for multi estimator search
     """
 
-    col_score = COL_SCORE  # + ("-",) * (ranking.columns.nlevels - len(COL_SCORE))
-    scores_actual: pd.Series = ranking.loc[:, col_score].values[: len(scores_expected)]
-    assert_array_almost_equal(
+    scores_actual: pd.Series = ranking.loc[:, COL_SCORE].values[: len(scores_expected)]
+
+    assert_allclose(
         scores_actual,
         scores_expected,
-        decimal=3,
+        rtol=0.01,
         err_msg=(
-            f"unexpected scores: " f"got {scores_actual} but expected {scores_expected}"
+            f"unexpected scores: got {scores_actual} but expected {scores_expected}"
         ),
     )
 
diff --git a/test/test/facet/test_inspection.py b/test/test/facet/test_inspection.py
index 76f0f795..69fe8fd5 100644
--- a/test/test/facet/test_inspection.py
+++ b/test/test/facet/test_inspection.py
@@ -3,7 +3,7 @@
 """
 import logging
 import warnings
-from typing import List, Optional, TypeVar, Union
+from typing import List, Optional, Set, TypeVar, Union
 
 import numpy as np
 import pandas as pd
@@ -11,10 +11,9 @@
 from numpy.testing import assert_allclose
 from pandas.testing import assert_frame_equal, assert_series_equal
 from sklearn.datasets import make_classification
-from sklearn.model_selection import GridSearchCV, KFold
+from sklearn.model_selection import GridSearchCV
 
 from pytools.viz.dendrogram import DendrogramDrawer, DendrogramReportStyle
-from sklearndf import TransformerDF
 from sklearndf.classification import (
     GradientBoostingClassifierDF,
     RandomForestClassifierDF,
@@ -37,31 +36,26 @@
 T = TypeVar("T")
 
 
-def test_model_inspection(
-    regressor_selector,
-    best_lgbm_model: RegressorPipelineDF,
-    preprocessed_feature_names,
-    regressor_inspector: LearnerInspector,
-    cv_kfold: KFold,
-    sample: Sample,
-    simple_preprocessor: TransformerDF,
-    n_jobs: int,
-) -> None:
-
-    ranking = regressor_selector.summary_report()
-
-    # define checksums for this test
-    log.debug(f"\n{ranking}")
-
+def test_regressor_selector(
+    regressor_selector: ModelSelector[RegressorPipelineDF, GridSearchCV]
+):
     check_ranking(
-        ranking=ranking,
+        ranking=regressor_selector.summary_report(),
         is_classifier=False,
         scores_expected=(
-            [0.693, 0.689, 0.677, 0.661, 0.615, 0.615, 0.367, 0.281, 0.281, 0.281]
+            [0.820, 0.818, 0.808, 0.806, 0.797, 0.797, 0.652, 0.651, 0.651, 0.651]
         ),
         params_expected=None,
     )
 
+
+def test_model_inspection(
+    best_lgbm_model: RegressorPipelineDF,
+    preprocessed_feature_names: Set[str],
+    regressor_inspector: LearnerInspector,
+    sample: Sample,
+    n_jobs: int,
+) -> None:
     shap_values: pd.DataFrame = regressor_inspector.shap_values()
 
     # the length of rows in shap_values should be equal to the unique observation