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GradientBoosting

Bases: Regressor

Gradient boosting regression.

Parameters:

Name Type Description Default
number_of_trees int

The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance.

 100
learning_rate float

The larger the value, the more the model is influenced by each additional tree. If the learning rate is too low, the model might underfit. If the learning rate is too high, the model might overfit.

 0.1

Raises:

Type Description
OutOfBoundsError

If number_of_trees or learning_rate are less than or equal to 0.
Source code in src/safeds/ml/classical/regression/_gradient_boosting.py 
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class GradientBoosting(Regressor):
    """
    Gradient boosting regression.

    Parameters
    ----------
    number_of_trees: int
        The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large
        number usually results in better performance.
    learning_rate : float
        The larger the value, the more the model is influenced by each additional tree. If the learning rate is too
        low, the model might underfit. If the learning rate is too high, the model might overfit.

    Raises
    ------
    OutOfBoundsError
        If `number_of_trees` or `learning_rate` are less than or equal to 0.
    """

    def __init__(self, *, number_of_trees: int = 100, learning_rate: float = 0.1) -> None:
        # Validation
        if number_of_trees < 1:
            raise OutOfBoundsError(number_of_trees, name="number_of_trees", lower_bound=ClosedBound(1))
        if learning_rate <= 0:
            raise OutOfBoundsError(learning_rate, name="learning_rate", lower_bound=OpenBound(0))

        # Hyperparameters
        self._number_of_trees = number_of_trees
        self._learning_rate = learning_rate

        # Internal state
        self._wrapped_regressor: sk_GradientBoostingRegressor | None = None
        self._feature_names: list[str] | None = None
        self._target_name: str | None = None

    @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:
        """
        Create a copy of this regressor and fit it with the given training data.

        This regressor is not modified.

        Parameters
        ----------
        training_set : TaggedTable
            The training data containing the feature and target vectors.

        Returns
        -------
        fitted_regressor : GradientBoosting
            The fitted regressor.

        Raises
        ------
        LearningError
            If the training data contains invalid values or if the training failed.
        UntaggedTableError
            If the table is untagged.
        NonNumericColumnError
            If the training data contains non-numerical values.
        MissingValuesColumnError
            If the training data contains missing values.
        DatasetMissesDataError
            If the training data contains no rows.
        """
        wrapped_regressor = self._get_sklearn_regressor()
        fit(wrapped_regressor, training_set)

        result = GradientBoosting(number_of_trees=self._number_of_trees, learning_rate=self._learning_rate)
        result._wrapped_regressor = wrapped_regressor
        result._feature_names = training_set.features.column_names
        result._target_name = training_set.target.name

        return result

    def predict(self, dataset: Table) -> TaggedTable:
        """
        Predict a target vector using a dataset containing feature vectors. The model has to be trained first.

        Parameters
        ----------
        dataset : Table
            The dataset containing the feature vectors.

        Returns
        -------
        table : TaggedTable
            A dataset containing the given feature vectors and the predicted target vector.

        Raises
        ------
        ModelNotFittedError
            If the model has not been fitted yet.
        DatasetContainsTargetError
            If the dataset contains the target column already.
        DatasetMissesFeaturesError
            If the dataset misses feature columns.
        PredictionError
            If predicting with the given dataset failed.
        NonNumericColumnError
            If the dataset contains non-numerical values.
        MissingValuesColumnError
            If the dataset contains missing values.
        DatasetMissesDataError
            If the dataset contains no rows.
        """
        return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)

    def is_fitted(self) -> bool:
        """
        Check if the regressor is fitted.

        Returns
        -------
        is_fitted : bool
            Whether the regressor is fitted.
        """
        return self._wrapped_regressor is not None

    def _get_sklearn_regressor(self) -> RegressorMixin:
        """
        Return a new wrapped Regressor from sklearn.

        Returns
        -------
        wrapped_regressor: RegressorMixin
            The sklearn Regressor.
        """
        return sk_GradientBoostingRegressor(n_estimators=self._number_of_trees, learning_rate=self._learning_rate)

learning_rate: float property

Get the learning rate.

Returns:

Name Type Description
result float

The learning rate.

number_of_trees: int property

Get the number of trees (estimators) in the ensemble.

Returns:

Name Type Description
result int

The number of trees.

__init__(*, number_of_trees=100, learning_rate=0.1)

Source code in src/safeds/ml/classical/regression/_gradient_boosting.py 
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def __init__(self, *, number_of_trees: int = 100, learning_rate: float = 0.1) -> None:
    # Validation
    if number_of_trees < 1:
        raise OutOfBoundsError(number_of_trees, name="number_of_trees", lower_bound=ClosedBound(1))
    if learning_rate <= 0:
        raise OutOfBoundsError(learning_rate, name="learning_rate", lower_bound=OpenBound(0))

    # Hyperparameters
    self._number_of_trees = number_of_trees
    self._learning_rate = learning_rate

    # Internal state
    self._wrapped_regressor: sk_GradientBoostingRegressor | None = None
    self._feature_names: list[str] | None = None
    self._target_name: str | None = None

fit(training_set)

Create a copy of this regressor and fit it with the given training data.

This regressor is not modified.

Parameters:

Name Type Description Default
training_set TaggedTable

The training data containing the feature and target vectors.

 required

Returns:

Name Type Description
fitted_regressor GradientBoosting

The fitted regressor.

Raises:

Type Description
LearningError

If the training data contains invalid values or if the training failed.
UntaggedTableError

If the table is untagged.
NonNumericColumnError

If the training data contains non-numerical values.
MissingValuesColumnError

If the training data contains missing values.
DatasetMissesDataError

If the training data contains no rows.
Source code in src/safeds/ml/classical/regression/_gradient_boosting.py 
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def fit(self, training_set: TaggedTable) -> GradientBoosting:
    """
    Create a copy of this regressor and fit it with the given training data.

    This regressor is not modified.

    Parameters
    ----------
    training_set : TaggedTable
        The training data containing the feature and target vectors.

    Returns
    -------
    fitted_regressor : GradientBoosting
        The fitted regressor.

    Raises
    ------
    LearningError
        If the training data contains invalid values or if the training failed.
    UntaggedTableError
        If the table is untagged.
    NonNumericColumnError
        If the training data contains non-numerical values.
    MissingValuesColumnError
        If the training data contains missing values.
    DatasetMissesDataError
        If the training data contains no rows.
    """
    wrapped_regressor = self._get_sklearn_regressor()
    fit(wrapped_regressor, training_set)

    result = GradientBoosting(number_of_trees=self._number_of_trees, learning_rate=self._learning_rate)
    result._wrapped_regressor = wrapped_regressor
    result._feature_names = training_set.features.column_names
    result._target_name = training_set.target.name

    return result

is_fitted()

Check if the regressor is fitted.

Returns:

Name Type Description
is_fitted bool

Whether the regressor is fitted.
Source code in src/safeds/ml/classical/regression/_gradient_boosting.py 
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def is_fitted(self) -> bool:
    """
    Check if the regressor is fitted.

    Returns
    -------
    is_fitted : bool
        Whether the regressor is fitted.
    """
    return self._wrapped_regressor is not None

predict(dataset)

Predict a target vector using a dataset containing feature vectors. The model has to be trained first.

Parameters:

Name Type Description Default
dataset Table

The dataset containing the feature vectors.

 required

Returns:

Name Type Description
table TaggedTable

A dataset containing the given feature vectors and the predicted target vector.

Raises:

Type Description
ModelNotFittedError

If the model has not been fitted yet.
DatasetContainsTargetError

If the dataset contains the target column already.
DatasetMissesFeaturesError

If the dataset misses feature columns.
PredictionError

If predicting with the given dataset failed.
NonNumericColumnError

If the dataset contains non-numerical values.
MissingValuesColumnError

If the dataset contains missing values.
DatasetMissesDataError

If the dataset contains no rows.
Source code in src/safeds/ml/classical/regression/_gradient_boosting.py 
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def predict(self, dataset: Table) -> TaggedTable:
    """
    Predict a target vector using a dataset containing feature vectors. The model has to be trained first.

    Parameters
    ----------
    dataset : Table
        The dataset containing the feature vectors.

    Returns
    -------
    table : TaggedTable
        A dataset containing the given feature vectors and the predicted target vector.

    Raises
    ------
    ModelNotFittedError
        If the model has not been fitted yet.
    DatasetContainsTargetError
        If the dataset contains the target column already.
    DatasetMissesFeaturesError
        If the dataset misses feature columns.
    PredictionError
        If predicting with the given dataset failed.
    NonNumericColumnError
        If the dataset contains non-numerical values.
    MissingValuesColumnError
        If the dataset contains missing values.
    DatasetMissesDataError
        If the dataset contains no rows.
    """
    return predict(self._wrapped_regressor, dataset, self._feature_names, self._target_name)