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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 (List[base.Classifier])

    The classifiers.

  • use_probabilities – defaults to 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: 87.14%

Methods

append

S.append(value) -- append value to the end of the sequence

Parameters

  • item
clear

S.clear() -> None -- remove all items from S

clone

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.

copy
count

S.count(value) -> integer -- return number of occurrences of value

Parameters

  • item
extend

S.extend(iterable) -- extend sequence by appending elements from the iterable

Parameters

  • other
index

S.index(value, [start, [stop]]) -> integer -- return first index of value. Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

Parameters

  • item
  • args
insert

S.insert(index, value) -- insert value before index

Parameters

  • i
  • item
learn_one

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

Parameters

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

Returns

Classifier: self

pop

S.pop([index]) -> item -- remove and return item at index (default last). Raise IndexError if list is empty or index is out of range.

Parameters

  • i – defaults to -1
predict_one

Predict the label of a set of features x.

Parameters

  • x (dict)

Returns

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

predict_proba_one

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

Parameters

  • x (dict)

Returns

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

remove

S.remove(value) -- remove first occurrence of value. Raise ValueError if the value is not present.

Parameters

  • item
reverse

S.reverse() -- reverse IN PLACE

sort