sklearn.ensemble
.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 fullX
whilefinal_estimator_
is trained using crossvalidated predictions of the base estimators usingcross_val_predict
.Read more in the User Guide.
New 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
. final_estimatorestimator, default=None
A classifier which will be used to combine the base estimators. The default classifier is a
LogisticRegression
. cvint, crossvalidation generator or an iterable, default=None
Determines the crossvalidation splitting strategy used in
cross_val_predict
to trainfinal_estimator
. Possible inputs for cv are:None, to use the default 5fold cross validation,
integer, to specify the number of folds in a (Stratified) KFold,
An object to be used as a crossvalidation 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. These splitters are instantiated withshuffle=False
so the splits will be the same across calls.Refer User Guide for the various crossvalidation 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_jobsint, default=None
The number of jobs to run in parallel all
estimators
fit
.None
means 1 unless in ajoblib.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, thefinal_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,)
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 inestimators_
. named_estimators_
Bunch
Attribute to access any fitted subestimators 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 forstack_method='auto'
or specifically forstack_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): 241259.
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...
Methods
Predict decision function for samples in X using
final_estimator_.decision_function
.fit
(X, y[, sample_weight])Fit the estimators.
fit_transform
(X[, y])Fit to data, then transform it.
get_params
([deep])Get the parameters of an estimator from the ensemble.
predict
(X, **predict_params)Predict target for X.
Predict class probabilities for X using
final_estimator_.predict_proba
.score
(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(**params)Set the parameters of an estimator from the ensemble.
transform
(X)Return class labels or probabilities for X for each estimator.

decision_function
(X)[source]¶ Predict decision function for samples in X using
final_estimator_.decision_function
. Parameters
 X{arraylike, 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_classes1) / 2)
The decision function computed the final estimator.

fit
(X, y, sample_weight=None)[source]¶ Fit the estimators.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
Training vectors, where
n_samples
is the number of samples andn_features
is the number of features. yarraylike of shape (n_samples,)
Target values.
 sample_weightarraylike 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

fit_transform
(X, y=None, **fit_params)[source]¶ Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
. Parameters
 Xarraylike of shape (n_samples, n_features)
Input samples.
 yarraylike 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_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.

predict
(X, **predict_params)[source]¶ Predict target for X.
 Parameters
 X{arraylike, 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 thefinal_estimator
. Note that this may be used to return uncertainties from some estimators withreturn_std
orreturn_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
final_estimator_.predict_proba
. Parameters
 X{arraylike, 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 multilabel 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
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for
X
. sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
Mean accuracy of
self.predict(X)
wrt.y
.

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 inestimators
. 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’.

transform
(X)[source]¶ Return class labels or probabilities for X for each estimator.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
Training vectors, where
n_samples
is the number of samples andn_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.