`sklearn.ensemble`.VotingClassifier#

class sklearn.ensemble.VotingClassifier(estimators, *, voting='hard', weights=None, n_jobs=None, flatten_transform=True, verbose=False)[source]#

Soft Voting/Majority Rule classifier for unfitted estimators.

See also

VotingRegressor: Prediction voting regressor.

Examples

>>> import numpy as np
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.ensemble import RandomForestClassifier, VotingClassifier
>>> clf1 = LogisticRegression(multi_class='multinomial', random_state=1)
>>> clf2 = RandomForestClassifier(n_estimators=50, random_state=1)
>>> clf3 = GaussianNB()
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> y = np.array([1, 1, 1, 2, 2, 2])
>>> eclf1 = VotingClassifier(estimators=[
...         ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard')
>>> eclf1 = eclf1.fit(X, y)
>>> print(eclf1.predict(X))
[1 1 1 2 2 2]
>>> np.array_equal(eclf1.named_estimators_.lr.predict(X),
...                eclf1.named_estimators_['lr'].predict(X))
True
>>> eclf2 = VotingClassifier(estimators=[
...         ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
...         voting='soft')
>>> eclf2 = eclf2.fit(X, y)
>>> print(eclf2.predict(X))
[1 1 1 2 2 2]

To drop an estimator, set_params can be used to remove it. Here we dropped one of the estimators, resulting in 2 fitted estimators:

>>> eclf2 = eclf2.set_params(lr='drop')
>>> eclf2 = eclf2.fit(X, y)
>>> len(eclf2.estimators_)
2

Setting flatten_transform=True with voting='soft' flattens output shape of transform:

>>> eclf3 = VotingClassifier(estimators=[
...        ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
...        voting='soft', weights=[2,1,1],
...        flatten_transform=True)
>>> eclf3 = eclf3.fit(X, y)
>>> print(eclf3.predict(X))
[1 1 1 2 2 2]
>>> print(eclf3.transform(X).shape)
(6, 6)

Methods

`fit`(X, y[, sample_weight])	Fit the estimators.
`fit_transform`(X[, y])	Return class labels or probabilities for each estimator.
`get_feature_names_out`([input_features])	Get output feature names for transformation.
`get_metadata_routing`()	Raise `NotImplementedError`.
`get_params`([deep])	Get the parameters of an estimator from the ensemble.
`predict`(X)	Predict class labels for X.
`predict_proba`(X)	Compute probabilities of possible outcomes for samples in X.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_fit_request`(*[, sample_weight])	Request metadata passed to the `fit` method.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of an estimator from the ensemble.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.
`transform`(X)	Return class labels or probabilities for X for each 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.
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.

New in version 0.18.

Returns:

selfobject: Returns the instance itself.

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

Return class labels or probabilities for each estimator.

Return predictions for X for each estimator.

Parameters:

X{array-like, sparse matrix, dataframe} of shape (n_samples, n_features): Input samples.
yndarray of shape (n_samples,), 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: Not used, present here for API consistency by convention.

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)[source]#

Predict class labels for X.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The input samples.

Returns:

majarray-like of shape (n_samples,): Predicted class labels.

predict_proba(X)[source]#

Compute probabilities of possible outcomes for samples in X.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The input samples.

Returns:

avgarray-like of shape (n_samples, n_classes): Weighted average probability for each class per sample.

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 = '$UNCHANGED$') → VotingClassifier[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.

New 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”}, 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

New 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 = '$UNCHANGED$') → VotingClassifier[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.

New 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:

probabilities_or_labels

If voting='soft' and flatten_transform=True:: returns ndarray of shape (n_samples, n_classifiers * n_classes), being class probabilities calculated by each classifier.
If voting='soft' and `flatten_transform=False:: ndarray of shape (n_classifiers, n_samples, n_classes)
If voting='hard':: ndarray of shape (n_samples, n_classifiers), being class labels predicted by each classifier.

Examples using `sklearn.ensemble.VotingClassifier`#

Plot class probabilities calculated by the VotingClassifier

Plot the decision boundaries of a VotingClassifier

sklearn.ensemble.VotingClassifier#

Examples using sklearn.ensemble.VotingClassifier#

`sklearn.ensemble`.VotingClassifier#

Examples using `sklearn.ensemble.VotingClassifier`#