feat: add suffixes to models to indicate their task (#588)

### Summary of Changes

Add the suffix `Classifier` to all models for classification and
`Regressor` to all models for regression. While being longer, this
naming has many advantages:

* Better **readability**: Several models have variants for
classification and regressions. Previously, both had the same name, so
to understand which one was used in the code, imports had to be checked.
* Better **auto-completion**: Now users can simply write `Classifier` or
`Regressor` to get a list of all suitable models.
* Better **understandability**: Now it's obvious, that logistic
regression is used for classification.

---------

Co-authored-by: megalinter-bot <129584137+megalinter-bot@users.noreply.github.com>

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:

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": {

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": {

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": {

src/safeds/ml/classical/classification/__init__.py

@@ -1,21 +1,21 @@
 """Classes for classification tasks."""
 
-from ._ada_boost import AdaBoost
+from ._ada_boost import AdaBoostClassifier
 from ._classifier import Classifier
-from ._decision_tree import DecisionTree
-from ._gradient_boosting import GradientBoosting
-from ._k_nearest_neighbors import KNearestNeighbors
-from ._logistic_regression import LogisticRegression
-from ._random_forest import RandomForest
-from ._support_vector_machine import SupportVectorMachine
+from ._decision_tree import DecisionTreeClassifier
+from ._gradient_boosting import GradientBoostingClassifier
+from ._k_nearest_neighbors import KNearestNeighborsClassifier
+from ._logistic_regression import LogisticRegressionClassifier
+from ._random_forest import RandomForestClassifier
+from ._support_vector_machine import SupportVectorMachineClassifier
 
 __all__ = [
-    "AdaBoost",
+    "AdaBoostClassifier",
     "Classifier",
-    "DecisionTree",
-    "GradientBoosting",
-    "KNearestNeighbors",
-    "LogisticRegression",
-    "RandomForest",
-    "SupportVectorMachine",
+    "DecisionTreeClassifier",
+    "GradientBoostingClassifier",
+    "KNearestNeighborsClassifier",
+    "LogisticRegressionClassifier",
+    "RandomForestClassifier",
+    "SupportVectorMachineClassifier",
 ]

src/safeds/ml/classical/classification/_ada_boost.py

@@ -15,7 +15,7 @@
     from safeds.data.tabular.containers import Table, TaggedTable
 
 
-class AdaBoost(Classifier):
+class AdaBoostClassifier(Classifier):
     """
     Ada Boost classification.
 
@@ -99,7 +99,7 @@ def learning_rate(self) -> float:
         """
         return self._learning_rate
 
-    def fit(self, training_set: TaggedTable) -> AdaBoost:
+    def fit(self, training_set: TaggedTable) -> AdaBoostClassifier:
         """
         Create a copy of this classifier and fit it with the given training data.
 
@@ -112,7 +112,7 @@ def fit(self, training_set: TaggedTable) -> AdaBoost:
 
         Returns
         -------
-        fitted_classifier : AdaBoost
+        fitted_classifier : AdaBoostClassifier
             The fitted classifier.
 
         Raises
@@ -131,7 +131,7 @@ def fit(self, training_set: TaggedTable) -> AdaBoost:
         wrapped_classifier = self._get_sklearn_classifier()
         fit(wrapped_classifier, training_set)
 
-        result = AdaBoost(
+        result = AdaBoostClassifier(
             learner=self.learner,
             maximum_number_of_learners=self.maximum_number_of_learners,
             learning_rate=self._learning_rate,

src/safeds/ml/classical/classification/_decision_tree.py

@@ -14,7 +14,7 @@
     from safeds.data.tabular.containers import Table, TaggedTable
 
 
-class DecisionTree(Classifier):
+class DecisionTreeClassifier(Classifier):
     """Decision tree classification."""
 
     def __init__(self) -> None:
@@ -23,7 +23,7 @@ def __init__(self) -> None:
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
-    def fit(self, training_set: TaggedTable) -> DecisionTree:
+    def fit(self, training_set: TaggedTable) -> DecisionTreeClassifier:
         """
         Create a copy of this classifier and fit it with the given training data.
 
@@ -36,7 +36,7 @@ def fit(self, training_set: TaggedTable) -> DecisionTree:
 
         Returns
         -------
-        fitted_classifier : DecisionTree
+        fitted_classifier : DecisionTreeClassifier
             The fitted classifier.
 
         Raises
@@ -55,7 +55,7 @@ def fit(self, training_set: TaggedTable) -> DecisionTree:
         wrapped_classifier = self._get_sklearn_classifier()
         fit(wrapped_classifier, training_set)
 
-        result = DecisionTree()
+        result = DecisionTreeClassifier()
         result._wrapped_classifier = wrapped_classifier
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

src/safeds/ml/classical/classification/_gradient_boosting.py

@@ -15,7 +15,7 @@
     from safeds.data.tabular.containers import Table, TaggedTable
 
 
-class GradientBoosting(Classifier):
+class GradientBoostingClassifier(Classifier):
     """
     Gradient boosting classification.
 
@@ -74,7 +74,7 @@ def learning_rate(self) -> float:
         """
         return self._learning_rate
 
-    def fit(self, training_set: TaggedTable) -> GradientBoosting:
+    def fit(self, training_set: TaggedTable) -> GradientBoostingClassifier:
         """
         Create a copy of this classifier and fit it with the given training data.
 
@@ -87,7 +87,7 @@ def fit(self, training_set: TaggedTable) -> GradientBoosting:
 
         Returns
         -------
-        fitted_classifier : GradientBoosting
+        fitted_classifier : GradientBoostingClassifier
             The fitted classifier.
 
         Raises
@@ -106,7 +106,7 @@ def fit(self, training_set: TaggedTable) -> GradientBoosting:
         wrapped_classifier = self._get_sklearn_classifier()
         fit(wrapped_classifier, training_set)
 
-        result = GradientBoosting(number_of_trees=self._number_of_trees, learning_rate=self._learning_rate)
+        result = GradientBoostingClassifier(number_of_trees=self._number_of_trees, learning_rate=self._learning_rate)
         result._wrapped_classifier = wrapped_classifier
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

src/safeds/ml/classical/classification/_k_nearest_neighbors.py

@@ -15,7 +15,7 @@
     from safeds.data.tabular.containers import Table, TaggedTable
 
 
-class KNearestNeighbors(Classifier):
+class KNearestNeighborsClassifier(Classifier):
     """
     K-nearest-neighbors classification.
 
@@ -56,7 +56,7 @@ def number_of_neighbors(self) -> int:
         """
         return self._number_of_neighbors
 
-    def fit(self, training_set: TaggedTable) -> KNearestNeighbors:
+    def fit(self, training_set: TaggedTable) -> KNearestNeighborsClassifier:
         """
         Create a copy of this classifier and fit it with the given training data.
 
@@ -69,7 +69,7 @@ def fit(self, training_set: TaggedTable) -> KNearestNeighbors:
 
         Returns
         -------
-        fitted_classifier : KNearestNeighbors
+        fitted_classifier : KNearestNeighborsClassifier
             The fitted classifier.
 
         Raises
@@ -99,7 +99,7 @@ def fit(self, training_set: TaggedTable) -> KNearestNeighbors:
         wrapped_classifier = self._get_sklearn_classifier()
         fit(wrapped_classifier, training_set)
 
-        result = KNearestNeighbors(self._number_of_neighbors)
+        result = KNearestNeighborsClassifier(self._number_of_neighbors)
         result._wrapped_classifier = wrapped_classifier
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

src/safeds/ml/classical/classification/_logistic_regression.py

@@ -14,7 +14,7 @@
     from safeds.data.tabular.containers import Table, TaggedTable
 
 
-class LogisticRegression(Classifier):
+class LogisticRegressionClassifier(Classifier):
     """Regularized logistic regression."""
 
     def __init__(self) -> None:
@@ -23,7 +23,7 @@ def __init__(self) -> None:
         self._feature_names: list[str] | None = None
         self._target_name: str | None = None
 
-    def fit(self, training_set: TaggedTable) -> LogisticRegression:
+    def fit(self, training_set: TaggedTable) -> LogisticRegressionClassifier:
         """
         Create a copy of this classifier and fit it with the given training data.
 
@@ -36,7 +36,7 @@ def fit(self, training_set: TaggedTable) -> LogisticRegression:
 
         Returns
         -------
-        fitted_classifier : LogisticRegression
+        fitted_classifier : LogisticRegressionClassifier
             The fitted classifier.
 
         Raises
@@ -55,7 +55,7 @@ def fit(self, training_set: TaggedTable) -> LogisticRegression:
         wrapped_classifier = self._get_sklearn_classifier()
         fit(wrapped_classifier, training_set)
 
-        result = LogisticRegression()
+        result = LogisticRegressionClassifier()
         result._wrapped_classifier = wrapped_classifier
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

src/safeds/ml/classical/classification/_random_forest.py

@@ -15,7 +15,7 @@
     from safeds.data.tabular.containers import Table, TaggedTable
 
 
-class RandomForest(Classifier):
+class RandomForestClassifier(Classifier):
     """Random forest classification.
 
     Parameters
@@ -54,7 +54,7 @@ def number_of_trees(self) -> int:
         """
         return self._number_of_trees
 
-    def fit(self, training_set: TaggedTable) -> RandomForest:
+    def fit(self, training_set: TaggedTable) -> RandomForestClassifier:
         """
         Create a copy of this classifier and fit it with the given training data.
 
@@ -67,7 +67,7 @@ def fit(self, training_set: TaggedTable) -> RandomForest:
 
         Returns
         -------
-        fitted_classifier : RandomForest
+        fitted_classifier : RandomForestClassifier
             The fitted classifier.
 
         Raises
@@ -86,7 +86,7 @@ def fit(self, training_set: TaggedTable) -> RandomForest:
         wrapped_classifier = self._get_sklearn_classifier()
         fit(wrapped_classifier, training_set)
 
-        result = RandomForest(number_of_trees=self._number_of_trees)
+        result = RandomForestClassifier(number_of_trees=self._number_of_trees)
         result._wrapped_classifier = wrapped_classifier
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name

src/safeds/ml/classical/classification/_support_vector_machine.py

@@ -30,7 +30,7 @@ def _get_sklearn_kernel(self) -> object:
         """
 
 
-class SupportVectorMachine(Classifier):
+class SupportVectorMachineClassifier(Classifier):
     """
     Support vector machine.
 
@@ -151,18 +151,18 @@ def _get_kernel_name(self) -> str:
         TypeError
             If the kernel type is invalid.
         """
-        if isinstance(self.kernel, SupportVectorMachine.Kernel.Linear):
+        if isinstance(self.kernel, SupportVectorMachineClassifier.Kernel.Linear):
             return "linear"
-        elif isinstance(self.kernel, SupportVectorMachine.Kernel.Polynomial):
+        elif isinstance(self.kernel, SupportVectorMachineClassifier.Kernel.Polynomial):
             return "poly"
-        elif isinstance(self.kernel, SupportVectorMachine.Kernel.Sigmoid):
+        elif isinstance(self.kernel, SupportVectorMachineClassifier.Kernel.Sigmoid):
             return "sigmoid"
-        elif isinstance(self.kernel, SupportVectorMachine.Kernel.RadialBasisFunction):
+        elif isinstance(self.kernel, SupportVectorMachineClassifier.Kernel.RadialBasisFunction):
             return "rbf"
         else:
             raise TypeError("Invalid kernel type.")
 
-    def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
+    def fit(self, training_set: TaggedTable) -> SupportVectorMachineClassifier:
         """
         Create a copy of this classifier and fit it with the given training data.
 
@@ -175,7 +175,7 @@ def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
 
         Returns
         -------
-        fitted_classifier : SupportVectorMachine
+        fitted_classifier : SupportVectorMachineClassifier
             The fitted classifier.
 
         Raises
@@ -194,7 +194,7 @@ def fit(self, training_set: TaggedTable) -> SupportVectorMachine:
         wrapped_classifier = self._get_sklearn_classifier()
         fit(wrapped_classifier, training_set)
 
-        result = SupportVectorMachine(c=self._c, kernel=self._kernel)
+        result = SupportVectorMachineClassifier(c=self._c, kernel=self._kernel)
         result._wrapped_classifier = wrapped_classifier
         result._feature_names = training_set.features.column_names
         result._target_name = training_set.target.name