StackingClassifier#

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

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.

Added in version 0.22.

Parameters:
estimatorslist 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_estimatorestimator, default=None

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

cvint, 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.

Added 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_jobsint, 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.

passthroughbool, 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.

verboseint, 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.

Added 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(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...
decision_function(X)[source]#

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:
decisionsndarray 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)[source]#

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.

yarray-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_weightarray-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:
selfobject

Returns a fitted instance of estimator.

fit_transform(X, y=None, **fit_params)[source]#

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:
Xarray-like of shape (n_samples, n_features)

Input samples.

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

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)[source]#

get output feature names for transformation.

Parameters:
input_featuresarray-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_outndarray of str objects

Transformed feature names.

get_metadata_routing()[source]#

Raise NotImplementedError.

This estimator does not support metadata routing yet.

get_params(deep=True)[source]#

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:
deepbool, default=True

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

Returns:
paramsdict

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)[source]#

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_paramsdict 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_predndarray of shape (n_samples,) or (n_samples, n_output)

Predicted targets.

predict_proba(X)[source]#

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:
probabilitiesndarray 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)[source]#

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:
Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:
scorefloat

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

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANgED$') → StackingClassifier[source]#

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.

Added 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_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANgED

Metadata routing for sample_weight parameter in fit.

Returns:
selfobject

The updated object.

set_output(*, transform=None)[source]#

Set output container.

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

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

Configure output of transform and fit_transform.

  • "default": Default output format of a transformer

  • "pandas": DataFrame output

  • "polars": Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: "polars" option was added.

Returns:
selfestimator instance

Estimator instance.

set_params(**params)[source]#

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:
**paramskeyword 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:
selfobject

Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANgED$') → StackingClassifier[source]#

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.

Added 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_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANgED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.

transform(X)[source]#

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_predsndarray of shape (n_samples, n_estimators) or (n_samples, n_classes * n_estimators)

Prediction outputs for each estimator.

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