feat: Add docstrings to the getter methods for hyperparameters in Reg…

…ression and Classification models (#371)

Closes #313

### Summary of Changes

* feat: Added docstrings in all getter methods of Hyperparameters for
classification and regression models.
* feat: Added docstrings in all methods of class Kernel in
SupportVectorMachine for classification and regression models.

---------

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

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

@@ -60,14 +60,38 @@ def __init__(
 
     @property
     def learner(self) -> Classifier | None:
+        """
+        Get the base learner used for training the ensemble.
+
+        Returns
+        -------
+        result: Classifier | None
+            The base learner.
+        """
         return self._learner
 
     @property
     def maximum_number_of_learners(self) -> int:
+        """
+        Get the maximum number of learners in the ensemble.
+
+        Returns
+        -------
+        result: int
+            The maximum number of learners.
+        """
         return self._maximum_number_of_learners
 
     @property
     def learning_rate(self) -> float:
+        """
+        Get the learning rate.
+
+        Returns
+        -------
+        result: float
+            The learning rate.
+        """
         return self._learning_rate
 
     def fit(self, training_set: TaggedTable) -> AdaBoost:

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

@@ -51,10 +51,26 @@ def __init__(self, *, number_of_trees: int = 100, learning_rate: float = 0.1) ->
 
     @property
     def number_of_trees(self) -> int:
+        """
+        Get the number of trees (estimators) in the ensemble.
+
+        Returns
+        -------
+        result: int
+            The number of trees.
+        """
         return self._number_of_trees
 
     @property
     def learning_rate(self) -> float:
+        """
+        Get the learning rate.
+
+        Returns
+        -------
+        result: float
+            The learning rate.
+        """
         return self._learning_rate
 
     def fit(self, training_set: TaggedTable) -> GradientBoosting:

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

@@ -46,6 +46,14 @@ def __init__(self, number_of_neighbors: int) -> None:
 
     @property
     def number_of_neighbors(self) -> int:
+        """
+        Get the number of neighbors used for interpolation.
+
+        Returns
+        -------
+        result: int
+            The number of neighbors.
+        """
         return self._number_of_neighbors
 
     def fit(self, training_set: TaggedTable) -> KNearestNeighbors:

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

@@ -43,6 +43,14 @@ def __init__(self, *, number_of_trees: int = 100) -> None:
 
     @property
     def number_of_trees(self) -> int:
+        """
+        Get the number of trees used in the random forest.
+
+        Returns
+        -------
+        result: int
+            The number of trees.
+        """
         return self._number_of_trees
 
     def fit(self, training_set: TaggedTable) -> RandomForest:

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

@@ -59,15 +59,39 @@ def __init__(self, *, c: float = 1.0, kernel: SupportVectorMachineKernel | None
 
     @property
     def c(self) -> float:
+        """
+        Get the regularization strength.
+
+        Returns
+        -------
+        result: float
+            The regularization strength.
+        """
         return self._c
 
     @property
     def kernel(self) -> SupportVectorMachineKernel | None:
+        """
+        Get the type of kernel used.
+
+        Returns
+        -------
+        result: SupportVectorMachineKernel | None
+            The type of kernel used.
+        """
         return self._kernel
 
     class Kernel:
         class Linear(SupportVectorMachineKernel):
             def get_sklearn_kernel(self) -> str:
+                """
+                Get the name of the linear kernel.
+
+                Returns
+                -------
+                result: str
+                    The name of the linear kernel.
+                """
                 return "linear"
 
         class Polynomial(SupportVectorMachineKernel):
@@ -77,17 +101,54 @@ def __init__(self, degree: int):
                 self._degree = degree
 
             def get_sklearn_kernel(self) -> str:
+                """
+                Get the name of the polynomial kernel.
+
+                Returns
+                -------
+                result: str
+                    The name of the polynomial kernel.
+                """
                 return "poly"
 
         class Sigmoid(SupportVectorMachineKernel):
             def get_sklearn_kernel(self) -> str:
+                """
+                Get the name of the sigmoid kernel.
+
+                Returns
+                -------
+                result: str
+                    The name of the sigmoid kernel.
+                """
                 return "sigmoid"
 
         class RadialBasisFunction(SupportVectorMachineKernel):
             def get_sklearn_kernel(self) -> str:
+                """
+                Get the name of the radial basis function (RBF) kernel.
+
+                Returns
+                -------
+                result: str
+                    The name of the RBF kernel.
+                """
                 return "rbf"
 
     def _get_kernel_name(self) -> str:
+        """
+        Get the name of the kernel.
+
+        Returns
+        -------
+        result: str
+            The name of the kernel.
+
+        Raises
+        ------
+        TypeError
+            If the kernel type is invalid.
+        """
         if isinstance(self.kernel, SupportVectorMachine.Kernel.Linear):
             return "linear"
         elif isinstance(self.kernel, SupportVectorMachine.Kernel.Polynomial):

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

@@ -60,14 +60,38 @@ def __init__(
 
     @property
     def learner(self) -> Regressor | None:
+        """
+        Get the base learner used for training the ensemble.
+
+        Returns
+        -------
+        result: Classifier | None
+            The base learner.
+        """
         return self._learner
 
     @property
     def maximum_number_of_learners(self) -> int:
+        """
+        Get the maximum number of learners in the ensemble.
+
+        Returns
+        -------
+        result: int
+            The maximum number of learners.
+        """
         return self._maximum_number_of_learners
 
     @property
     def learning_rate(self) -> float:
+        """
+        Get the learning rate.
+
+        Returns
+        -------
+        result: float
+            The learning rate.
+        """
         return self._learning_rate
 
     def fit(self, training_set: TaggedTable) -> AdaBoost:

src/safeds/ml/classical/regression/_elastic_net_regression.py

@@ -76,10 +76,26 @@ def __init__(self, *, alpha: float = 1.0, lasso_ratio: float = 0.5) -> None:
 
     @property
     def alpha(self) -> float:
+        """
+        Get the regularization of the model.
+
+        Returns
+        -------
+        result: float
+            The regularization of the model.
+        """
         return self._alpha
 
     @property
     def lasso_ratio(self) -> float:
+        """
+        Get the ratio between Lasso and Ridge regularization.
+
+        Returns
+        -------
+        result: float
+            The ratio between Lasso and Ridge regularization.
+        """
         return self._lasso_ratio
 
     def fit(self, training_set: TaggedTable) -> ElasticNetRegression:

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

@@ -51,10 +51,26 @@ def __init__(self, *, number_of_trees: int = 100, learning_rate: float = 0.1) ->
 
     @property
     def number_of_trees(self) -> int:
+        """
+        Get the number of trees (estimators) in the ensemble.
+
+        Returns
+        -------
+        result: int
+            The number of trees.
+        """
         return self._number_of_trees
 
     @property
     def learning_rate(self) -> float:
+        """
+        Get the learning rate.
+
+        Returns
+        -------
+        result: float
+            The learning rate.
+        """
         return self._learning_rate
 
     def fit(self, training_set: TaggedTable) -> GradientBoosting:

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

@@ -46,6 +46,14 @@ def __init__(self, number_of_neighbors: int) -> None:
 
     @property
     def number_of_neighbors(self) -> int:
+        """
+        Get the number of neighbors used for interpolation.
+
+        Returns
+        -------
+        result: int
+            The number of neighbors.
+        """
         return self._number_of_neighbors
 
     def fit(self, training_set: TaggedTable) -> KNearestNeighbors:

src/safeds/ml/classical/regression/_lasso_regression.py

@@ -53,6 +53,14 @@ def __init__(self, *, alpha: float = 1.0) -> None:
 
     @property
     def alpha(self) -> float:
+        """
+        Get the regularization of the model.
+
+        Returns
+        -------
+        result: float
+            The regularization of the model.
+        """
         return self._alpha
 
     def fit(self, training_set: TaggedTable) -> LassoRegression:

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

@@ -43,6 +43,14 @@ def __init__(self, *, number_of_trees: int = 100) -> None:
 
     @property
     def number_of_trees(self) -> int:
+        """
+        Get the number of trees used in the random forest.
+
+        Returns
+        -------
+        result: int
+            The number of trees.
+        """
         return self._number_of_trees
 
     def fit(self, training_set: TaggedTable) -> RandomForest:

src/safeds/ml/classical/regression/_ridge_regression.py

@@ -54,6 +54,14 @@ def __init__(self, *, alpha: float = 1.0) -> None:
 
     @property
     def alpha(self) -> float:
+        """
+        Get the regularization of the model.
+
+        Returns
+        -------
+        result: float
+            The regularization of the model.
+        """
         return self._alpha
 
     def fit(self, training_set: TaggedTable) -> RidgeRegression: