DriftRetrainingClassifier

Drift retraining classifier.

This classifier is a wrapper for any classifier. It monitors the incoming data for concept drifts and warnings in the model's accurary. In case a warning is detected, a background model starts to train. If a drift is detected, the model will be replaced by the background model, and the background model will be reset.

Parameters

• model ('base.Classifier')

  The classifier and background classifier class.

• drift_detector ('base.DriftAndWarningDetector | base.BinaryDriftAndWarningDetector | None') – defaults to None

  Algorithm to track warnings and concept drifts. Attention! If the parameter train_in_background is True, the drift_detector must have a warning tracker.

• train_in_background ('bool') – defaults to True

  Parameter to determine if a background model will be used.

Examples

>>> from river import datasets
>>> from river import evaluate
>>> from river import drift
>>> from river import metrics
>>> from river import tree

>>> dataset = datasets.Elec2().take(3000)

>>> model = drift.DriftRetrainingClassifier(
...     model=tree.HoeffdingTreeClassifier(),
...     drift_detector=drift.binary.DDM()
... )

>>> metric = metrics.Accuracy()

>>> evaluate.progressive_val_score(dataset, model, metric)
Accuracy: 86.40%

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

typing.Union[bool, str, int, NoneType]: 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.