VotingClassifier (original) (raw)
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 attributeself.estimators_. An estimator can be set to 'drop' usingset_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 Glossaryfor 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 estimatorsthat 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.
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.
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 oftransform:
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, *, sample_weight=None, **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 andn_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.
Added in version 0.18.
**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 usingsklearn.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 n_features_in_#
Number of features seen during fit.
property named_estimators#
Dictionary to access any fitted sub-estimators by name.
Returns:
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.
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 ifenable_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 tofitif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it tofit.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.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside aPipeline. 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 APIfor 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 outputNone: Transform configuration is unchanged
Added in version 1.4: "polars" option was added.
Returns:
selfestimator instance
Estimator instance.
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 inestimators.
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 ifenable_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 toscoreif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it toscore.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.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside aPipeline. 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.
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 andn_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.