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# `sklearn.ensemble`.StackingClassifier

class sklearn.ensemble.StackingClassifier(estimators, final_estimator=None, *, cv=None, stack_method='auto', n_jobs=None, passthrough=False, verbose=0)

  Stack of estimators with a final classifier.

  Stacked generalization consists in stacking the output of individual estimator and use a classifier to compute the final prediction. Stacking allows to use the strength of each individual estimator by using their output as input of a final estimator.

  Note that `estimators_` are fitted on the full `X` while `final_estimator_` is trained using cross-validated predictions of the base estimators using `cross_val_predict`.

  Read more in the User Guide.

  New in version 0.22.

  Parameters:
    estimators list of (str, estimator)

      Base estimators which will be stacked together. Each element of the list is defined as a tuple of string (i.e. name) and an estimator instance. An estimator can be set to ‘drop’ using `set_params`.

      The type of estimator is generally expected to be a classifier. However, one can pass a regressor for some use case (e.g. ordinal regression).

    final_estimator estimator, default=None

      A classifier which will be used to combine the base estimators. The default classifier is a `LogisticRegression`.

    cv int, cross-validation generator, iterable, or “prefit”, default=None

      Determines the cross-validation splitting strategy used in `cross_val_predict` to train `final_estimator`. Possible inputs for cv are:

      - None, to use the default 5-fold cross validation,

      - integer, to specify the number of folds in a (Stratified) KFold,

      - An object to be used as a cross-validation generator,

      - An iterable yielding train, test splits,

      - `"prefit"` to assume the `estimators` are prefit. In this case, the estimators will not be refitted.

      For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, `StratifiedKFold` is used. In all other cases, `KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls.

      Refer User Guide for the various cross-validation strategies that can be used here.

      If “prefit” is passed, it is assumed that all `estimators` have been fitted already. The `final_estimator_` is trained on the `estimators` predictions on the full training set and are not cross validated predictions. Please note that if the models have been trained on the same data to train the stacking model, there is a very high risk of overfitting.

      New in version 1.1: The ‘prefit’ option was added in 1.1

      Note

      A larger number of split will provide no benefits if the number of training samples is large enough. Indeed, the training time will increase. `cv` is not used for model evaluation but for prediction.

    stack_method{‘auto’, ‘predict_proba’, ‘decision_function’, ‘predict’}, default=’auto’

      Methods called for each base estimator. It can be:

      - if ‘auto’, it will try to invoke, for each estimator, `'predict_proba'`, `'decision_function'` or `'predict'` in that order.

      - otherwise, one of `'predict_proba'`, `'decision_function'` or `'predict'`. If the method is not implemented by the estimator, it will raise an error.

    n_jobs int, default=None

      The number of jobs to run in parallel all `estimators` `fit`. `None` means 1 unless in a `joblib.parallel_backend` context. -1 means using all processors. See Glossary for more details.

    passthrough bool, default=False

      When False, only the predictions of estimators will be used as training data for `final_estimator`. When True, the `final_estimator` is trained on the predictions as well as the original training data.

    verbose int, default=0

      Verbosity level.

  Attributes:
    classes_ndarray of shape (n_classes,) or list of ndarray if `y` is of type `"multilabel-indicator"`.

      Class labels.

    estimators_list of estimators

      The elements of the `estimators` parameter, having been fitted on the training data. If an estimator has been set to `'drop'`, it will not appear in `estimators_`. When `cv="prefit"`, `estimators_` is set to `estimators` and is not fitted again.

    named_estimators_`Bunch`

      Attribute to access any fitted sub-estimators by name.

    `n_features_in_`int

      Number of features seen during fit.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Only defined if the underlying estimators expose such an attribute when fit.

      New in version 1.0.

    final_estimator_estimator

      The classifier which predicts given the output of `estimators_`.

    stack_method_list of str

      The method used by each base estimator.

  See also

  `StackingRegressor`

    Stack of estimators with a final regressor.

  Notes

  When `predict_proba` is used by each estimator (i.e. most of the time for `stack_method='auto'` or specifically for `stack_method='predict_proba'`), The first column predicted by each estimator will be dropped in the case of a binary classification problem. Indeed, both feature will be perfectly collinear.

  In some cases (e.g. ordinal regression), one can pass regressors as the first layer of the `StackingClassifier`. However, note that `y` will be internally encoded in a numerically increasing order or lexicographic order. If this ordering is not adequate, one should manually numerically encode the classes in the desired order.

  References

  [1]

  Wolpert, David H. “Stacked generalization.” Neural networks 5.2 (1992): 241-259.

  Examples

      >>> from sklearn.datasets import load_iris
      >>> from sklearn.ensemble import RandomForestClassifier
      >>> from sklearn.svm import LinearSVC
      >>> from sklearn.linear_model import LogisticRegression
      >>> from sklearn.preprocessing import StandardScaler
      >>> from sklearn.pipeline import make_pipeline
      >>> from sklearn.ensemble import StackingClassifier
      >>> X, y = load_iris(return_X_y=True)
      >>> estimators = [
      ...     ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),
      ...     ('svr', make_pipeline(StandardScaler(),
      ...                           LinearSVC(dual="auto", random_state=42)))
      ... ]
      >>> clf = StackingClassifier(
      ...     estimators=estimators, final_estimator=LogisticRegression()
      ... )
      >>> from sklearn.model_selection import train_test_split
      >>> X_train, X_test, y_train, y_test = train_test_split(
      ...     X, y, stratify=y, random_state=42
      ... )
      >>> clf.fit(X_train, y_train).score(X_test, y_test)
      0.9...

  Methods

  `decision_function`(X) | Decision function for samples in `X` using the final estimator.
  `fit`(X, y[, sample_weight]) | Fit the estimators.
  `fit_transform`(X[, y]) | Fit to data, then transform it.
  `get_feature_names_out`([input_features]) | Get output feature names for transformation.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get the parameters of an estimator from the ensemble.
  `predict`(X, **predict_params) | Predict target for X.
  `predict_proba`(X) | Predict class probabilities for `X` using the final estimator.
  `score`(X, y[, sample_weight]) | Return the mean accuracy on the given test data and labels.
  `set_fit_request`(*[, sample_weight]) | Request metadata passed to the `fit` method.
  `set_output`(*[, transform]) | Set output container.
  `set_params`(**params) | Set the parameters of an estimator from the ensemble.
  `set_score_request`(*[, sample_weight]) | Request metadata passed to the `score` method.
  `transform`(X) | Return class labels or probabilities for X for each estimator.

  decision_function(X)

    Decision function for samples in `X` using the final estimator.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features.

    Returns:
      decisions ndarray of shape (n_samples,), (n_samples, n_classes), or (n_samples, n_classes * (n_classes-1) / 2)

        The decision function computed the final estimator.

  fit(X, y, sample_weight=None)

    Fit the estimators.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features.

      y array-like of shape (n_samples,)

        Target values. Note that `y` will be internally encoded in numerically increasing order or lexicographic order. If the order matter (e.g. for ordinal regression), one should numerically encode the target `y` before calling fit.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights. If None, then samples are equally weighted. Note that this is supported only if all underlying estimators support sample weights.

    Returns:
      self object

        Returns a fitted instance of estimator.

  fit_transform(X, y=None, **fit_params)

    Fit to data, then transform it.

    Fits transformer to `X` and `y` with optional parameters `fit_params` and returns a transformed version of `X`.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Input samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs), default=None

        Target values (None for unsupervised transformations).

      **fit_params dict

        Additional fit parameters.

    Returns:
      X_new ndarray array of shape (n_samples, n_features_new)

        Transformed array.

  get_feature_names_out(input_features=None)

    Get output feature names for transformation.

    Parameters:
      input_features array-like of str or None, default=None

        Input features. The input feature names are only used when `passthrough` is `True`.

        - If `input_features` is `None`, then `feature_names_in_` is used as feature names in. If `feature_names_in_` is not defined, then names are generated: `[x0, x1, ..., x(n_features_in_ - 1)]`.

        - If `input_features` is an array-like, then `input_features` must match `feature_names_in_` if `feature_names_in_` is defined.

        If `passthrough` is `False`, then only the names of `estimators` are used to generate the output feature names.

    Returns:
      feature_names_out ndarray of str objects

        Transformed feature names.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get the parameters of an estimator from the ensemble.

    Returns the parameters given in the constructor as well as the estimators contained within the `estimators` parameter.

    Parameters:
      deep bool, default=True

        Setting it to True gets the various estimators and the parameters of the estimators as well.

    Returns:
      params dict

        Parameter and estimator names mapped to their values or parameter names mapped to their values.

  property n_features_in_

    Number of features seen during fit.

  property named_estimators

    Dictionary to access any fitted sub-estimators by name.

    Returns:
      `Bunch`
  predict(X, **predict_params)

    Predict target for X.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features.

      **predict_params dict of str -> obj

        Parameters to the `predict` called by the `final_estimator`. Note that this may be used to return uncertainties from some estimators with `return_std` or `return_cov`. Be aware that it will only accounts for uncertainty in the final estimator.

    Returns:
      y_pred ndarray of shape (n_samples,) or (n_samples, n_output)

        Predicted targets.

  predict_proba(X)

    Predict class probabilities for `X` using the final estimator.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features.

    Returns:
      probabilities ndarray of shape (n_samples, n_classes) or list of ndarray of shape (n_output,)

        The class probabilities of the input samples.

  score(X, y, sample_weight=None)

    Return the mean accuracy on the given test data and labels.

    In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

    Parameters:
      X array-like of shape (n_samples, n_features)

        Test samples.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True labels for `X`.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

        Mean accuracy of `self.predict(X)` w.r.t. `y`.

  set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StackingClassifier

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_output(*, transform=None)

    Set output container.

    See Introducing the set_output API for an example on how to use the API.

    Parameters:
      transform{“default”, “pandas”}, default=None

        Configure output of `transform` and `fit_transform`.

        - `"default"`: Default output format of a transformer

        - `"pandas"`: DataFrame output

        - `None`: Transform configuration is unchanged

    Returns:
      self estimator instance

        Estimator instance.

  set_params(**params)

    Set the parameters of an estimator from the ensemble.

    Valid parameter keys can be listed with `get_params()`. Note that you can directly set the parameters of the estimators contained in `estimators`.

    Parameters:
      **params keyword arguments

        Specific parameters using e.g. `set_params(parameter_name=new_value)`. In addition, to setting the parameters of the estimator, the individual estimator of the estimators can also be set, or can be removed by setting them to ‘drop’.

    Returns:
      self object

        Estimator instance.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → StackingClassifier

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

  transform(X)

    Return class labels or probabilities for X for each estimator.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features.

    Returns:
      y_preds ndarray of shape (n_samples, n_estimators) or (n_samples, n_classes * n_estimators)

        Prediction outputs for each estimator.

## Examples using `sklearn.ensemble.StackingClassifier`

Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.22