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Voting classifier.

A classification is made by aggregating the predictions of each model in the ensemble. The probabilities for each class are summed up if use_probabilities is set to True. If not, the probabilities are ignored and each prediction is weighted the same. In this case, it's important that you use an odd number of classifiers. A random class will be picked if the number of classifiers is even.


  • models (List[base.Classifier])

    The classifiers.

  • use_probabilities – defaults to True

    Whether or to weight each prediction with its associated probability.


>>> from river import datasets
>>> from river import ensemble
>>> from river import evaluate
>>> from river import linear_model
>>> from river import metrics
>>> from river import naive_bayes
>>> from river import preprocessing
>>> from river import tree

>>> dataset = datasets.Phishing()

>>> model = (
...     preprocessing.StandardScaler() |
...     ensemble.VotingClassifier(
...         models=[
...             linear_model.LogisticRegression(),
...             tree.HoeffdingTreeClassifier(),
...             naive_bayes.GaussianNB()
...         ]
...     )
... )

>>> metric = metrics.F1()

>>> evaluate.progressive_val_score(dataset, model, metric)
F1: 0.871429



Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.


Update the model with a set of features x and a label y.


  • x (dict)
  • y (Union[bool, str, int])


Classifier: self


Predict the label of a set of features x.


  • x (dict)


typing.Union[bool, str, int]: The predicted label.


Predict the probability of each label for a dictionary of features x.


  • x (dict)


typing.Dict[typing.Union[bool, str, int], float]: A dictionary that associates a probability which each label.