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 → base.Classifier

  The classifier to boost.

• n_models

  Default → 10

  The number of models in the ensemble.

• seed

  Type → int | None

  Default → 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: 87.41%

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 — '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.

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1. Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard Gavaldà. "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. 105–112. Morgan Kaufmann, 2001. ↩