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ADWINBoostingClassifier

ADWIN Boosting classifier.

ADWIN Boosting 1 is the online boosting method of Oza and Russell 2 with the addition of the ADWIN algorithm as a change detector. If concept drift is detected, the worst member of the ensemble (based on the error estimation by ADWIN) is replaced by a new (empty) classifier.

Parameters

  • model

    Type โ†’ base.Classifier

    The classifier to boost.

  • n_models

    Default โ†’ 10

    The number of models in the ensemble.

  • seed

    Type โ†’ int | None

    Default โ†’ None

    Random number generator seed for reproducibility.

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 preprocessing

dataset = datasets.Phishing()
model = ensemble.ADWINBoostingClassifier(
    model=(
        preprocessing.StandardScaler() |
        linear_model.LogisticRegression()
    ),
    n_models=3,
    seed=42
)
metric = metrics.F1()

evaluate.progressive_val_score(dataset, model, metric)
F1: 87.61%

Methods

learn_one

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

Parameters

  • x
  • y
  • kwargs

Returns

self

predict_one

Predict the label of a set of features x.

Parameters

  • x โ€” 'dict'
  • kwargs

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
  • kwargs

Returns

A dictionary that associates a probability which each label.


  1. Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard Gavaldร . "New ensemble methods for evolving data streams." In 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009. 

  2. Oza, N., Russell, S. "Online bagging and boosting." In: Artificial Intelligence and Statistics 2001, pp. 105โ€“112. Morgan Kaufmann, 2001.