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.

Read more in the User Guide.

Added in version 0.17.

Parameters:
estimatorslist of (str, estimator) tuples

Invoking the fit method on the VotingClassifier will fit clones of those original estimators that will be stored in the class attribute self.estimators_. An estimator can be set to 'drop' using set_params.

Changed in version 0.21: 'drop' is accepted. Using None was deprecated in 0.22 and support was removed in 0.24.

voting{‘hard’, ‘soft’}, default=’hard’

If ‘hard’, uses predicted class labels for majority rule voting. Else if ‘soft’, predicts the class label based on the argmax of the sums of the predicted probabilities, which is recommended for an ensemble of well-calibrated classifiers.

weightsarray-like of shape (n_classifiers,), default=None

Sequence of weights (float or int) to weight the occurrences of predicted class labels (hard voting) or class probabilities before averaging (soft voting). Uses uniform weights if None.

n_jobsint, default=None

The number of jobs to run in parallel for fit. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

Added in version 0.18.

flatten_transformbool, default=True

Affects shape of transform output only when voting=’soft’ If voting=’soft’ and flatten_transform=True, transform method returns matrix with shape (n_samples, n_classifiers * n_classes). If flatten_transform=False, it returns (n_classifiers, n_samples, n_classes).

verbosebool, default=False

If True, the time elapsed while fitting will be printed as it is completed.

Added in version 0.23.

Attributes:
estimators_list of classifiers

The collection of fitted sub-estimators as defined in estimators that are not ‘drop’.

named_estimators_Bunch

Attribute to access any fitted sub-estimators by name.

Added in version 0.20.

le_LabelEncoder

Transformer used to encode the labels during fit and decode during prediction.

classes_ndarray of shape (n_classes,)

The classes labels.

n_features_in_int

Number of features seen during fit.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Only defined if the underlying estimators expose such an attribute when fit.

Added in version 1.0.

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(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)
fit(X, y, **fit_params)[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.

**fit_paramsdict

Parameters to pass to the underlying estimators.

Added in version 1.5: Only available if enable_metadata_routing=True, which can be set by using sklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.

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]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Added in version 1.5.

Returns:
routingMetadataRouter

A MetadataRouter encapsulating routing information.

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 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 accuracy on provided 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_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”, “polars”}, 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

Added 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]#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the 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.

Added in version 1.3.

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.