VotingClassifier

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.

Parameters

• models

  Type → list[base.Classifier]

  The classifiers.

• use_probabilities

  Default → True

  Whether or to weight each prediction with its associated probability.

Attributes

• models

Examples

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([
        linear_model.LogisticRegression(),
        tree.HoeffdingTreeClassifier(),
        naive_bayes.GaussianNB()
    ])
)

metric = metrics.F1()

evaluate.progressive_val_score(dataset, model, metric)

F1: 86.94%

Methods

learn_one

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

Parameters

• x — 'dict[base.typing.FeatureName, Any]'
• y — 'base.typing.ClfTarget'

predict_one

Predict the label of a set of features x.

Parameters

• x — 'dict[base.typing.FeatureName, Any]'

Returns

base.typing.ClfTarget | None: The predicted label.

predict_proba_one

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

Parameters

• x — 'dict[base.typing.FeatureName, Any]'
• kwargs — 'Any'

Returns

dict[base.typing.ClfTarget, float]: A dictionary that associates a probability which each label.