`sklearn.ensemble`.VotingClassifier¶

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

Soft Voting/Majority Rule classifier for unfitted estimators.

New in version 0.17.

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]
>>> 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`(self, X, y[, sample_weight])	Fit the estimators.
`fit_transform`(self, X[, y])	Fit to data, then transform it.
`get_params`(self[, deep])	Get the parameters of the ensemble estimator
`predict`(self, X)	Predict class labels for X.
`score`(self, X, y[, sample_weight])	Returns the mean accuracy on the given test data and labels.
`set_params`(self, \\params)	Setting the parameters for the ensemble estimator
`transform`(self, X)	Return class labels or probabilities for X for each estimator.

__init__(self, estimators, voting='hard', weights=None, n_jobs=None, flatten_transform=True)[source]¶: Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y, sample_weight=None)[source]¶

Fit the estimators.

Parameters

X{array-like, sparse matrix}, 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, shape (n_samples,): Target values.
sample_weightarray-like, shape (n_samples,) or 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(self, X, y=None, **fit_params)[source]¶

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

Xnumpy array of shape [n_samples, n_features]: Training set.
ynumpy array of shape [n_samples]: Target values.

Returns

X_newnumpy array of shape [n_samples, n_features_new]: Transformed array.

get_params(self, deep=True)[source]¶

Get the parameters of the ensemble estimator

Parameters

deepbool: Setting it to True gets the various estimators and the parameters of the estimators as well

predict(self, X)[source]¶

Predict class labels for X.

Parameters

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

Returns

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

property predict_proba¶

Compute probabilities of possible outcomes for samples in X.

Parameters

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

Returns

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

score(self, X, y, sample_weight=None)[source]¶

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

Xarray-like, shape = (n_samples, n_features): Test samples.
yarray-like, shape = (n_samples) or (n_samples, n_outputs): True labels for X.
sample_weightarray-like, shape = [n_samples], optional: Sample weights.

Returns

scorefloat: Mean accuracy of self.predict(X) wrt. y.

set_params(self, **params)[source]¶

Setting the parameters for the ensemble estimator

Valid parameter keys can be listed with get_params().

Parameters

**paramskeyword arguments: Specific parameters using e.g. set_params(parameter_name=new_value) In addition, to setting the parameters of the ensemble estimator, the individual estimators of the ensemble estimator can also be set or replaced by setting them to None.

Examples

# In this example, the RandomForestClassifier is removed clf1 = LogisticRegression() clf2 = RandomForestClassifier() eclf = VotingClassifier(estimators=[(‘lr’, clf1), (‘rf’, clf2)] eclf.set_params(rf=None)

transform(self, X)[source]¶

Return class labels or probabilities for X for each estimator.

Parameters

X{array-like, sparse matrix}, 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 array-like of shape (n_classifiers, n_samples * n_classes), being class probabilities calculated by each classifier.
If voting='soft' and `flatten_transform=False:: array-like of shape (n_classifiers, n_samples, n_classes)
If voting='hard':: array-like of shape (n_samples, n_classifiers), being class labels predicted by each classifier.

sklearn.ensemble.VotingClassifier¶

`sklearn.ensemble`.VotingClassifier¶