package sklearn

Stack of estimators with a final regressor.

Stacked generalization consists in stacking the output of individual estimator and use a regressor 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 regressor which will be used to combine the base estimators. The default regressor is a `RidgeCV`.

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.

n_jobs : int, default=None The number of jobs to run in parallel for `fit` of all `estimators`. `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 ---------- estimators_ : list of estimator 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 regressor to stacked the base estimators fitted.

References ---------- .. 1 Wolpert, David H. 'Stacked generalization.' Neural networks 5.2 (1992): 241-259.

Examples -------- >>> from sklearn.datasets import load_diabetes >>> from sklearn.linear_model import RidgeCV >>> from sklearn.svm import LinearSVR >>> from sklearn.ensemble import RandomForestRegressor >>> from sklearn.ensemble import StackingRegressor >>> X, y = load_diabetes(return_X_y=True) >>> estimators = ... ('lr', RidgeCV()), ... ('svr', LinearSVR(random_state=42)) ... >>> reg = StackingRegressor( ... estimators=estimators, ... final_estimator=RandomForestRegressor(n_estimators=10, ... random_state=42) ... ) >>> from sklearn.model_selection import train_test_split >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=42 ... ) >>> reg.fit(X_train, y_train).score(X_test, y_test) 0.3...

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.

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns ------- score : float R^2 of self.predict(X) wrt. y.

Notes ----- The R2 score used when calling ``score`` on a regressor uses ``multioutput='uniform_average'`` from version 0.23 to keep consistent with default value of :func:`~sklearn.metrics.r2_score`. This influences the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`).

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 the predictions 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) Prediction outputs for each estimator.

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.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.