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`.

.. versionadded:: 0.22

Read more in the :ref:`User Guide <stacking>`.

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`.

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 or an iterable, 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.

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.

Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here.

.. 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,) 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_`.

named_estimators_ : :class:`~sklearn.utils.Bunch` Attribute to access any fitted sub-estimators by name.

final_estimator_ : estimator The classifier which predicts given the output of `estimators_`.

stack_method_ : list of str The method used by each base estimator.

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.

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...

Predict decision function for samples in X using `final_estimator_.decision_function`.

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 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.

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

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, sparse matrix, dataframe of shape (n_samples, n_features)

y : ndarray of shape (n_samples,), default=None Target values.

**fit_params : dict Additional fit parameters.

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

Get the parameters of an estimator from the ensemble.

Parameters ---------- deep : bool, default=True Setting it to True gets the various classifiers and the parameters of the classifiers as well.

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 class probabilities for X using `final_estimator_.predict_proba`.

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.

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) wrt. y.

Set the parameters of an estimator from the ensemble.

Valid parameter keys can be listed with `get_params()`.

Parameters ---------- **params : keyword arguments Specific parameters using e.g. `set_params(parameter_name=new_value)`. In addition, to setting the parameters of the stacking estimator, the individual estimator of the stacking estimators can also be set, or can be removed by setting them to 'drop'.

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.

Attribute classes_: get value or raise Not_found if None.

Attribute classes_: get value as an option.

Attribute estimators_: get value or raise Not_found if None.

Attribute estimators_: get value as an option.

Attribute named_estimators_: get value or raise Not_found if None.

Attribute named_estimators_: get value as an option.

Attribute final_estimator_: get value or raise Not_found if None.

Attribute final_estimator_: get value as an option.

Attribute stack_method_: get value or raise Not_found if None.

Attribute stack_method_: get value as an option.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.