AdaBoostRegressor

Bases: Regressor, _AdaBoostBase

Ada Boost regression.

Parameters:

Name	Type	Description	Default
learner	Regressor | None | Choice[Regressor | None]	The learner from which the boosted ensemble is built.	None
max_learner_count	int | Choice[int]	The maximum number of learners at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early. Has to be greater than 0.	50
learning_rate	float | Choice[float]	Weight applied to each regressor at each boosting iteration. A higher learning rate increases the contribution of each regressor. Has to be greater than 0.	1.0

Raises:

Type	Description
OutOfBoundsError	If max_learner_count or learning_rate are less than or equal to 0.

Source code in src/safeds/ml/classical/regression/_ada_boost_regressor.py

class AdaBoostRegressor(Regressor, _AdaBoostBase):
    """
    Ada Boost regression.

    Parameters
    ----------
    learner:
        The learner from which the boosted ensemble is built.
    max_learner_count:
        The maximum number of learners at which boosting is terminated. In case of perfect fit, the learning procedure
        is stopped early. Has to be greater than 0.
    learning_rate:
        Weight applied to each regressor at each boosting iteration. A higher learning rate increases the contribution
        of each regressor. Has to be greater than 0.

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

    # ------------------------------------------------------------------------------------------------------------------
    # Dunder methods
    # ------------------------------------------------------------------------------------------------------------------

    def __init__(
        self,
        *,
        learner: Regressor | None | Choice[Regressor | None] = None,
        max_learner_count: int | Choice[int] = 50,
        learning_rate: float | Choice[float] = 1.0,
    ) -> None:
        # Initialize superclasses
        Regressor.__init__(self)
        _AdaBoostBase.__init__(
            self,
            max_learner_count=max_learner_count,
            learning_rate=learning_rate,
        )

        # Hyperparameters
        self._learner: Regressor | None | Choice[Regressor | None] = learner

    def __hash__(self) -> int:
        return _structural_hash(
            Regressor.__hash__(self),
            _AdaBoostBase.__hash__(self),
            self._learner,
        )

    # ------------------------------------------------------------------------------------------------------------------
    # Properties
    # ------------------------------------------------------------------------------------------------------------------

    @property
    def learner(self) -> Regressor | None | Choice[Regressor | None]:  # type: ignore[override]
        """The base learner used for training the ensemble."""
        return self._learner

    # ------------------------------------------------------------------------------------------------------------------
    # Template methods
    # ------------------------------------------------------------------------------------------------------------------

    def _clone(self) -> AdaBoostRegressor:
        return AdaBoostRegressor(
            learner=self.learner,
            max_learner_count=self._max_learner_count,
            learning_rate=self._learning_rate,
        )

    def _get_sklearn_model(self) -> RegressorMixin:
        from sklearn.ensemble import AdaBoostRegressor as SklearnAdaBoostRegressor

        assert not isinstance(self.learner, Choice)
        learner = self.learner._get_sklearn_model() if self.learner is not None else None
        return SklearnAdaBoostRegressor(
            estimator=learner,
            n_estimators=self._max_learner_count,
            learning_rate=self._learning_rate,
        )

    def _check_additional_fit_preconditions(self) -> None:
        if (
            isinstance(self._max_learner_count, Choice)
            or isinstance(self._learning_rate, Choice)
            or isinstance(self._learner, Choice)
        ):
            raise FittingWithChoiceError

    def _check_additional_fit_by_exhaustive_search_preconditions(self) -> None:
        if (
            not isinstance(self._max_learner_count, Choice)
            and not isinstance(self._learning_rate, Choice)
            and not isinstance(self._learner, Choice)
        ):
            raise FittingWithoutChoiceError

    def _get_models_for_all_choices(self) -> list[AdaBoostRegressor]:
        learner_choices = self._learner if isinstance(self._learner, Choice) else [self._learner]
        max_learner_count_choices = (
            self._max_learner_count if isinstance(self._max_learner_count, Choice) else [self._max_learner_count]
        )
        learning_rate_choices = (
            self._learning_rate if isinstance(self._learning_rate, Choice) else [self._learning_rate]
        )

        models = []
        for learner in learner_choices:
            for mlc in max_learner_count_choices:
                for lr in learning_rate_choices:
                    models.append(AdaBoostRegressor(learner=learner, max_learner_count=mlc, learning_rate=lr))
        return models

is_fitted: bool

Whether the model is fitted.

learner: Regressor | None | Choice[Regressor | None]

The base learner used for training the ensemble.

learning_rate: float | Choice[float]

The learning rate.

max_learner_count: int | Choice[int]

The maximum number of learners in the ensemble.

coefficient_of_determination

Compute the coefficient of determination (R²) of the regressor on the given data.

The coefficient of determination compares the regressor's predictions to another model that always predicts the mean of the target values. It is a measure of how well the regressor explains the variance in the target values.

The higher the coefficient of determination, the better the regressor. Results range from negative infinity to 1.0. You can interpret the coefficient of determination as follows:

R²	Interpretation
1.0	The model perfectly predicts the target values. Did you overfit?
(0.0, 1.0)	The model is better than predicting the mean of the target values. You should be here.
0.0	The model is as good as predicting the mean of the target values. Try something else.
(-∞, 0.0)	The model is worse than predicting the mean of the target values. Something is very wrong.

Notes:

• The model must be fitted.
• Some other libraries call this metric r2_score.

Parameters:

Name	Type	Description	Default
validation_or_test_set	Table | TabularDataset	The validation or test set.	required

Returns:

Name	Type	Description
coefficient_of_determination	float	The coefficient of determination of the regressor.

Raises:

Type	Description
ModelNotFittedError	If the classifier has not been fitted yet.

Source code in src/safeds/ml/classical/regression/_regressor.py

def coefficient_of_determination(self, validation_or_test_set: Table | TabularDataset) -> float:
    """
    Compute the coefficient of determination (R²) of the regressor on the given data.

    The coefficient of determination compares the regressor's predictions to another model that always predicts the
    mean of the target values. It is a measure of how well the regressor explains the variance in the target values.

    The **higher** the coefficient of determination, the better the regressor. Results range from negative infinity
    to 1.0. You can interpret the coefficient of determination as follows:

    | R²         | Interpretation                                                                             |
    | ---------- | ------------------------------------------------------------------------------------------ |
    | 1.0        | The model perfectly predicts the target values. Did you overfit?                           |
    | (0.0, 1.0) | The model is better than predicting the mean of the target values. You should be here.     |
    | 0.0        | The model is as good as predicting the mean of the target values. Try something else.      |
    | (-∞, 0.0)  | The model is worse than predicting the mean of the target values. Something is very wrong. |

    **Notes:**

    - The model must be fitted.
    - Some other libraries call this metric `r2_score`.

    Parameters
    ----------
    validation_or_test_set:
        The validation or test set.

    Returns
    -------
    coefficient_of_determination:
        The coefficient of determination of the regressor.

    Raises
    ------
    ModelNotFittedError
        If the classifier has not been fitted yet.
    """
    if not self.is_fitted:
        raise ModelNotFittedError

    validation_or_test_set = _extract_table(validation_or_test_set)

    return RegressionMetrics.coefficient_of_determination(
        self.predict(validation_or_test_set),
        validation_or_test_set.get_column(self.get_target_name()),
    )

fit

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

Note: This model is not modified.

Parameters:

Name	Type	Description	Default
training_set	TabularDataset	The training data containing the features and target.	required

Returns:

Name	Type	Description
fitted_model	Self	The fitted model.

Raises:

Type	Description
PlainTableError	If a table is passed instead of a TabularDataset.
DatasetMissesDataError	If the given training set contains no data.
FittingWithChoiceError	When trying to call this method on a model with hyperparameter choices.
LearningError	If the training data contains invalid values or if the training failed.

Source code in src/safeds/ml/classical/_supervised_model.py

def fit(self, training_set: TabularDataset) -> Self:
    """
    Create a copy of this model and fit it with the given training data.

    **Note:** This model is not modified.

    Parameters
    ----------
    training_set:
        The training data containing the features and target.

    Returns
    -------
    fitted_model:
        The fitted model.

    Raises
    ------
    PlainTableError
        If a table is passed instead of a TabularDataset.
    DatasetMissesDataError
        If the given training set contains no data.
    FittingWithChoiceError
        When trying to call this method on a model with hyperparameter choices.
    LearningError
        If the training data contains invalid values or if the training failed.
    """
    if not isinstance(training_set, TabularDataset) and isinstance(training_set, Table):
        raise PlainTableError
    if training_set.to_table().row_count == 0:
        raise DatasetMissesDataError

    self._check_additional_fit_preconditions()
    self._check_more_additional_fit_preconditions(training_set)

    wrapped_model = self._get_sklearn_model()
    _fit_sklearn_model_in_place(wrapped_model, training_set)

    result = self._clone()
    result._feature_schema = training_set.features.schema
    result._target_name = training_set.target.name
    result._target_type = training_set.target.type
    result._wrapped_model = wrapped_model

    return result

fit_by_exhaustive_search

Use the hyperparameter choices to create multiple models and fit them.

Note: This model is not modified.

Parameters:

Name	Type	Description	Default
training_set	TabularDataset	The training data containing the features and target.	required
optimization_metric	RegressorMetric	The metric that should be used for determining the performance of a model.	required

Returns:

Name	Type	Description
best_model	Self	The model that performed the best out of all possible models given the Choices of hyperparameters.

Raises:

Type	Description
PlainTableError	If a table is passed instead of a TabularDataset.
DatasetMissesDataError	If the given training set contains no data.
FittingWithoutChoiceError	When trying to call this method on a model without hyperparameter choices.
LearningError	If the training data contains invalid values or if the training failed.

Source code in src/safeds/ml/classical/regression/_regressor.py

def fit_by_exhaustive_search(self, training_set: TabularDataset, optimization_metric: RegressorMetric) -> Self:
    """
    Use the hyperparameter choices to create multiple models and fit them.

    **Note:** This model is not modified.

    Parameters
    ----------
    training_set:
        The training data containing the features and target.
    optimization_metric:
        The metric that should be used for determining the performance of a model.

    Returns
    -------
    best_model:
        The model that performed the best out of all possible models given the Choices of hyperparameters.

    Raises
    ------
    PlainTableError
        If a table is passed instead of a TabularDataset.
    DatasetMissesDataError
        If the given training set contains no data.
    FittingWithoutChoiceError
        When trying to call this method on a model without hyperparameter choices.
    LearningError
        If the training data contains invalid values or if the training failed.
    """
    if training_set.to_table().row_count == 0:
        raise DatasetMissesDataError

    self._check_additional_fit_by_exhaustive_search_preconditions()

    [train_split, test_split] = training_set.to_table().split_rows(0.75)
    train_data = train_split.to_tabular_dataset(
        target_name=training_set.target.name,
        extra_names=training_set.extras.column_names,
    )
    test_data = test_split.to_tabular_dataset(
        target_name=training_set.target.name,
        extra_names=training_set.extras.column_names,
    )

    list_of_models = self._get_models_for_all_choices()
    list_of_fitted_models = []

    with ProcessPoolExecutor(max_workers=len(list_of_models), mp_context=mp.get_context("spawn")) as executor:
        futures = []
        for model in list_of_models:
            futures.append(executor.submit(model.fit, train_data))
        [done, _] = wait(futures, return_when=ALL_COMPLETED)
        for future in done:
            list_of_fitted_models.append(future.result())
    executor.shutdown()

    best_model = None
    best_metric_value = None
    for fitted_model in list_of_fitted_models:
        if best_model is None:
            best_model = fitted_model
            match optimization_metric.value:
                case "mean_squared_error":
                    best_metric_value = fitted_model.mean_squared_error(test_data)
                case "mean_absolute_error":
                    best_metric_value = fitted_model.mean_absolute_error(test_data)
                case "median_absolute_deviation":
                    best_metric_value = fitted_model.median_absolute_deviation(test_data)
                case "coefficient_of_determination":
                    best_metric_value = fitted_model.coefficient_of_determination(test_data)
        else:
            match optimization_metric.value:
                case "mean_squared_error":
                    error_of_fitted_model = fitted_model.mean_squared_error(test_data)
                    if error_of_fitted_model < best_metric_value:
                        best_model = fitted_model  # pragma: no cover
                        best_metric_value = error_of_fitted_model  # pragma: no cover
                case "mean_absolute_error":
                    error_of_fitted_model = fitted_model.mean_absolute_error(test_data)
                    if error_of_fitted_model < best_metric_value:
                        best_model = fitted_model  # pragma: no cover
                        best_metric_value = error_of_fitted_model  # pragma: no cover
                case "median_absolute_deviation":
                    error_of_fitted_model = fitted_model.median_absolute_deviation(test_data)
                    if error_of_fitted_model < best_metric_value:
                        best_model = fitted_model  # pragma: no cover
                        best_metric_value = error_of_fitted_model  # pragma: no cover
                case "coefficient_of_determination":
                    error_of_fitted_model = fitted_model.coefficient_of_determination(test_data)
                    if error_of_fitted_model > best_metric_value:
                        best_model = fitted_model  # pragma: no cover
                        best_metric_value = error_of_fitted_model  # pragma: no cover
    assert best_model is not None
    return best_model

get_feature_names

Return the names of the feature columns.

Note: The model must be fitted.

Returns:

Name	Type	Description
feature_names	list[str]	The names of the feature columns.

Raises:

Type	Description
ModelNotFittedError	If the model has not been fitted yet.

Source code in src/safeds/ml/classical/_supervised_model.py

def get_feature_names(self) -> list[str]:
    """
    Return the names of the feature columns.

    **Note:** The model must be fitted.

    Returns
    -------
    feature_names:
        The names of the feature columns.

    Raises
    ------
    ModelNotFittedError
        If the model has not been fitted yet.
    """
    # Used in favor of is_fitted, so the type checker is happy
    if self._feature_schema is None:
        raise ModelNotFittedError

    return self._feature_schema.column_names

get_features_schema

Return the schema of the feature columns.

Note: The model must be fitted.

Returns:

Name	Type	Description
feature_schema	Schema	The schema of the feature columns.

Raises:

Type	Description
ModelNotFittedError	If the model has not been fitted yet.

Source code in src/safeds/ml/classical/_supervised_model.py

def get_features_schema(self) -> Schema:
    """
    Return the schema of the feature columns.

    **Note:** The model must be fitted.

    Returns
    -------
    feature_schema:
        The schema of the feature columns.

    Raises
    ------
    ModelNotFittedError
        If the model has not been fitted yet.
    """
    # Used in favor of is_fitted, so the type checker is happy
    if self._feature_schema is None:
        raise ModelNotFittedError

    return self._feature_schema

get_target_name

Return the name of the target column.

Note: The model must be fitted.

Returns:

Name	Type	Description
target_name	str	The name of the target column.

Raises:

Type	Description
ModelNotFittedError	If the model has not been fitted yet.

Source code in src/safeds/ml/classical/_supervised_model.py

def get_target_name(self) -> str:
    """
    Return the name of the target column.

    **Note:** The model must be fitted.

    Returns
    -------
    target_name:
        The name of the target column.

    Raises
    ------
    ModelNotFittedError
        If the model has not been fitted yet.
    """
    # Used in favor of is_fitted, so the type checker is happy
    if self._target_name is None:
        raise ModelNotFittedError

    return self._target_name

get_target_type

Return the type of the target column.

Note: The model must be fitted.

Returns:

Name	Type	Description
target_type	DataType	The type of the target column.

Raises:

Type	Description
ModelNotFittedError	If the model has not been fitted yet.

Source code in src/safeds/ml/classical/_supervised_model.py

def get_target_type(self) -> DataType:
    """
    Return the type of the target column.

    **Note:** The model must be fitted.

    Returns
    -------
    target_type:
        The type of the target column.

    Raises
    ------
    ModelNotFittedError
        If the model has not been fitted yet.
    """
    # Used in favor of is_fitted, so the type checker is happy
    if self._target_type is None:
        raise ModelNotFittedError

    return self._target_type

mean_absolute_error

Compute the mean absolute error (MAE) of the regressor on the given data.

The mean absolute error is the average of the absolute differences between the predicted and expected target values. The lower the mean absolute error, the better the regressor. Results range from 0.0 to positive infinity.

Note: The model must be fitted.

Parameters:

Name	Type	Description	Default
validation_or_test_set	Table | TabularDataset	The validation or test set.	required

Returns:

Name	Type	Description
mean_absolute_error	float	The mean absolute error of the regressor.

Raises:

Type	Description
ModelNotFittedError	If the classifier has not been fitted yet.

Source code in src/safeds/ml/classical/regression/_regressor.py

def mean_absolute_error(self, validation_or_test_set: Table | TabularDataset) -> float:
    """
    Compute the mean absolute error (MAE) of the regressor on the given data.

    The mean absolute error is the average of the absolute differences between the predicted and expected target
    values. The **lower** the mean absolute error, the better the regressor. Results range from 0.0 to positive
    infinity.

    **Note:** The model must be fitted.

    Parameters
    ----------
    validation_or_test_set:
        The validation or test set.

    Returns
    -------
    mean_absolute_error:
        The mean absolute error of the regressor.

    Raises
    ------
    ModelNotFittedError
        If the classifier has not been fitted yet.
    """
    if not self.is_fitted:
        raise ModelNotFittedError

    validation_or_test_set = _extract_table(validation_or_test_set)

    return RegressionMetrics.mean_absolute_error(
        self.predict(validation_or_test_set),
        validation_or_test_set.get_column(self.get_target_name()),
    )

mean_directional_accuracy

Compute the mean directional accuracy (MDA) of the regressor on the given data.

This metric compares two consecutive target values and checks if the predicted direction (down/unchanged/up) matches the expected direction. The mean directional accuracy is the proportion of correctly predicted directions. The higher the mean directional accuracy, the better the regressor. Results range from 0.0 to 1.0.

This metric is useful for time series data, where the order of the target values has a meaning. It is not useful for other types of data. Because of this, it is not included in the summarize_metrics method.

Note: The model must be fitted.

Parameters:

Name	Type	Description	Default
validation_or_test_set	Table | TabularDataset	The validation or test set.	required

Returns:

Name	Type	Description
mean_directional_accuracy	float	The mean directional accuracy of the regressor.

Raises:

Type	Description
ModelNotFittedError	If the classifier has not been fitted yet.

Source code in src/safeds/ml/classical/regression/_regressor.py

def mean_directional_accuracy(self, validation_or_test_set: Table | TabularDataset) -> float:
    """
    Compute the mean directional accuracy (MDA) of the regressor on the given data.

    This metric compares two consecutive target values and checks if the predicted direction (down/unchanged/up)
    matches the expected direction. The mean directional accuracy is the proportion of correctly predicted
    directions. The **higher** the mean directional accuracy, the better the regressor. Results range from 0.0 to
    1.0.

    This metric is useful for time series data, where the order of the target values has a meaning. It is not useful
    for other types of data. Because of this, it is not included in the `summarize_metrics` method.

    **Note:** The model must be fitted.

    Parameters
    ----------
    validation_or_test_set:
        The validation or test set.

    Returns
    -------
    mean_directional_accuracy:
        The mean directional accuracy of the regressor.

    Raises
    ------
    ModelNotFittedError
        If the classifier has not been fitted yet.
    """
    if not self.is_fitted:
        raise ModelNotFittedError

    validation_or_test_set = _extract_table(validation_or_test_set)

    return RegressionMetrics.mean_directional_accuracy(
        self.predict(validation_or_test_set),
        validation_or_test_set.get_column(self.get_target_name()),
    )

mean_squared_error

Compute the mean squared error (MSE) of the regressor on the given data.

The mean squared error is the average of the squared differences between the predicted and expected target values. The lower the mean squared error, the better the regressor. Results range from 0.0 to positive infinity.

NoteS:

• The model must be fitted.
• To get the root mean squared error (RMSE), take the square root of the result.

Parameters:

Name	Type	Description	Default
validation_or_test_set	Table | TabularDataset	The validation or test set.	required

Returns:

Name	Type	Description
mean_squared_error	float	The mean squared error of the regressor.

Raises:

Type	Description
ModelNotFittedError	If the classifier has not been fitted yet.

Source code in src/safeds/ml/classical/regression/_regressor.py

def mean_squared_error(self, validation_or_test_set: Table | TabularDataset) -> float:
    """
    Compute the mean squared error (MSE) of the regressor on the given data.

    The mean squared error is the average of the squared differences between the predicted and expected target
    values. The **lower** the mean squared error, the better the regressor. Results range from 0.0 to positive
    infinity.

    **NoteS:**

    - The model must be fitted.
    - To get the root mean squared error (RMSE), take the square root of the result.

    Parameters
    ----------
    validation_or_test_set:
        The validation or test set.

    Returns
    -------
    mean_squared_error:
        The mean squared error of the regressor.

    Raises
    ------
    ModelNotFittedError
        If the classifier has not been fitted yet.
    """
    if not self.is_fitted:
        raise ModelNotFittedError

    validation_or_test_set = _extract_table(validation_or_test_set)

    return RegressionMetrics.mean_squared_error(
        self.predict(validation_or_test_set),
        validation_or_test_set.get_column(self.get_target_name()),
    )

median_absolute_deviation

Compute the median absolute deviation (MAD) of the regressor on the given data.

The median absolute deviation is the median of the absolute differences between the predicted and expected target values. The lower the median absolute deviation, the better the regressor. Results range from 0.0 to positive infinity.

Note: The model must be fitted.

Parameters:

Name	Type	Description	Default
validation_or_test_set	Table | TabularDataset	The validation or test set.	required

Returns:

Name	Type	Description
median_absolute_deviation	float	The median absolute deviation of the regressor.

Raises:

Type	Description
ModelNotFittedError	If the classifier has not been fitted yet.

Source code in src/safeds/ml/classical/regression/_regressor.py

def median_absolute_deviation(self, validation_or_test_set: Table | TabularDataset) -> float:
    """
    Compute the median absolute deviation (MAD) of the regressor on the given data.

    The median absolute deviation is the median of the absolute differences between the predicted and expected
    target values. The **lower** the median absolute deviation, the better the regressor. Results range from 0.0 to
    positive infinity.

    **Note:** The model must be fitted.

    Parameters
    ----------
    validation_or_test_set:
        The validation or test set.

    Returns
    -------
    median_absolute_deviation:
        The median absolute deviation of the regressor.

    Raises
    ------
    ModelNotFittedError
        If the classifier has not been fitted yet.
    """
    if not self.is_fitted:
        raise ModelNotFittedError

    validation_or_test_set = _extract_table(validation_or_test_set)

    return RegressionMetrics.median_absolute_deviation(
        self.predict(validation_or_test_set),
        validation_or_test_set.get_column(self.get_target_name()),
    )

predict

Predict the target values on the given dataset.

Note: The model must be fitted.

Parameters:

Name	Type	Description	Default
dataset	Table | TabularDataset	The dataset containing at least the features.	required

Returns:

Name	Type	Description
prediction	TabularDataset	The given dataset with an additional column for the predicted target values.

Raises:

Type	Description
ModelNotFittedError	If the model has not been fitted yet.
DatasetMissesFeaturesError	If the dataset misses feature columns.
PredictionError	If predicting with the given dataset failed.

Source code in src/safeds/ml/classical/_supervised_model.py

def predict(
    self,
    dataset: Table | TabularDataset,
) -> TabularDataset:
    """
    Predict the target values on the given dataset.

    **Note:** The model must be fitted.

    Parameters
    ----------
    dataset:
        The dataset containing at least the features.

    Returns
    -------
    prediction:
        The given dataset with an additional column for the predicted target values.

    Raises
    ------
    ModelNotFittedError
        If the model has not been fitted yet.
    DatasetMissesFeaturesError
        If the dataset misses feature columns.
    PredictionError
        If predicting with the given dataset failed.
    """
    self._check_additional_predict_preconditions(dataset)

    return _predict_with_sklearn_model(
        self._wrapped_model,
        dataset,
        self.get_feature_names(),
        self.get_target_name(),
    )

summarize_metrics

Summarize the regressor's metrics on the given data.

Note: The model must be fitted.

Parameters:

Name	Type	Description	Default
validation_or_test_set	Table | TabularDataset	The validation or test set.	required

Returns:

Name	Type	Description
metrics	Table	A table containing the regressor's metrics.

Raises:

Type	Description
ModelNotFittedError	If the classifier has not been fitted yet.

Source code in src/safeds/ml/classical/regression/_regressor.py

def summarize_metrics(self, validation_or_test_set: Table | TabularDataset) -> Table:
    """
    Summarize the regressor's metrics on the given data.

    **Note:** The model must be fitted.

    Parameters
    ----------
    validation_or_test_set:
        The validation or test set.

    Returns
    -------
    metrics:
        A table containing the regressor's metrics.

    Raises
    ------
    ModelNotFittedError
        If the classifier has not been fitted yet.
    """
    if not self.is_fitted:
        raise ModelNotFittedError

    validation_or_test_set = _extract_table(validation_or_test_set)

    return RegressionMetrics.summarize(
        self.predict(validation_or_test_set),
        validation_or_test_set.get_column(self.get_target_name()),
    )