diff --git a/docs/glossary.md b/docs/glossary.md
index f0afeb80e..01c358560 100644
--- a/docs/glossary.md
+++ b/docs/glossary.md
@@ -25,7 +25,7 @@ It classifies the predictions to be either be [true positive](#true-positive-tp)
 ## Decision Tree
 A Decision Tree represents the process of conditional evaluation in a tree diagram.
 
-Implemented in Safe-DS as [Decision Tree][safeds.ml.classical.classification.DecisionTree].
+Implemented in Safe-DS as [DecisionTreeClassifier][safeds.ml.classical.classification.DecisionTreeClassifier] and [DecisionTreeRegressor][safeds.ml.classical.regression.DecisionTreeRegressor].
 
 ## F1-Score
 The harmonic mean of [precision](#precision) and [recall](#recall). Formula:
@@ -48,7 +48,7 @@ It is analogous to a column within a table.
 Linear Regression is the supervised Machine Learning model in which the model finds the best fit linear line between the independent and dependent variable
 i.e. it finds the linear relationship between the dependent and independent variable.
 
-Implemented in Safe-DS as [LinearRegression][safeds.ml.classical.regression.LinearRegression].
+Implemented in Safe-DS as [LinearRegression][safeds.ml.classical.regression.LinearRegressionRegressor].
 
 ## Machine Learning (ML)
 Machine Learning is a generic term for artificially generating knowledge through experience.
@@ -84,7 +84,7 @@ See here for respective references:
 ## Random Forest
 Random Forest is an ML model that works by generating decision trees at random.
 
-Implemented in Safe-DS as [RandomForest][safeds.ml.classical.regression.RandomForest].
+Implemented in Safe-DS as [RandomForestClassifier][safeds.ml.classical.classification.RandomForestClassifier] and [RandomForestRegressor][safeds.ml.classical.regression.RandomForestRegressor].
 
 ## Recall
 The ability of a [classification](#classification) model to identify all the relevant data points. Formula:
diff --git a/docs/tutorials/classification.ipynb b/docs/tutorials/classification.ipynb
index 59e2b812b..89f994afd 100644
--- a/docs/tutorials/classification.ipynb
+++ b/docs/tutorials/classification.ipynb
@@ -145,9 +145,9 @@
    "execution_count": null,
    "outputs": [],
    "source": [
-    "from safeds.ml.classical.classification import RandomForest\n",
+    "from safeds.ml.classical.classification import RandomForestClassifier\n",
     "\n",
-    "model = RandomForest()\n",
+    "model = RandomForestClassifier()\n",
     "fitted_model= model.fit(tagged_train_table)"
    ],
    "metadata": {
diff --git a/docs/tutorials/machine_learning.ipynb b/docs/tutorials/machine_learning.ipynb
index f9d53bca1..acb5d3e27 100644
--- a/docs/tutorials/machine_learning.ipynb
+++ b/docs/tutorials/machine_learning.ipynb
@@ -54,9 +54,9 @@
    "execution_count": null,
    "outputs": [],
    "source": [
-    "from safeds.ml.classical.regression import LinearRegression\n",
+    "from safeds.ml.classical.regression import LinearRegressionRegressor\n",
     "\n",
-    "model = LinearRegression()\n",
+    "model = LinearRegressionRegressor()\n",
     "fitted_model = model.fit(tagged_table)"
    ],
    "metadata": {
diff --git a/docs/tutorials/regression.ipynb b/docs/tutorials/regression.ipynb
index 7b26f0ee3..21aaba3de 100644
--- a/docs/tutorials/regression.ipynb
+++ b/docs/tutorials/regression.ipynb
@@ -98,9 +98,9 @@
    "execution_count": null,
    "outputs": [],
    "source": [
-    "from safeds.ml.classical.regression import DecisionTree\n",
+    "from safeds.ml.classical.regression import DecisionTreeRegressor\n",
     "\n",
-    "model = DecisionTree()\n",
+    "model = DecisionTreeRegressor()\n",
     "fitted_model = model.fit(tagged_train_table)"
    ],
    "metadata": {