StackingClassifier

StackingClassifier(classifiers, meta_classifier, use_probas=False, average_probas=False, verbose=0, use_features_in_secondary=False, store_train_meta_features=False, use_clones=True)

A Stacking classifier for scikit-learn estimators for classification.

Parameters

Attributes

Examples

For usage examples, please see http://rasbt.github.io/mlxtend/user_guide/classifier/StackingClassifier/

Methods


fit(X, y, sample_weight=None)

Fit ensemble classifers and the meta-classifier.

Parameters

Returns


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

Returns


get_params(deep=True)

Return estimator parameter names for GridSearch support.


predict(X)

Predict target values for X.

Parameters

Returns


predict_meta_features(X)

Get meta-features of test-data.

Parameters

Returns


predict_proba(X)

Predict class probabilities for X.

Parameters

Returns


score(X, y, sample_weight=None)

Returns 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

Returns


set_params(params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Returns

self