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. Read more in the User Guide.  New 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.  New 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.  New 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.  New in version 0.20.   
classes_array-like of shape (n_predictions,) 

The classes labels.      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(X, y[, sample_weight]) Fit the estimators.  
fit_transform(X[, y]) Return class labels or probabilities for each estimator.  
get_params([deep]) Get the parameters of an estimator from the ensemble.  
predict(X) Predict class labels for X.  
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.    
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 
   
  
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_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) [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.     
  
property predict_proba  
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) 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 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’.     
  
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_classifiers, n_samples * 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 the decision boundaries of a VotingClassifier  

  Plot class probabilities calculated by the VotingClassifier