OnlineSmoothBoost#

class capymoa.classifier.OnlineSmoothBoost[source]#

Bases: MOAClassifier

Online Smooth Boost.

Online Smooth Boost [1] is a ensemble classifier. Incremental on-line boosting with Theoretical Justifications of Shang-Tse Chen.

>>> from capymoa.classifier import OnlineSmoothBoost
>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.evaluation import prequential_evaluation
>>>
>>> stream = ElectricityTiny()
>>> classifier = OnlineSmoothBoost(stream.get_schema())
>>> results = prequential_evaluation(stream, classifier, max_instances=1000)
>>> print(f"{results['cumulative'].accuracy():.1f}")
87.8
__init__(
schema: Schema | None = None,
random_seed: int = 0,
base_learner='trees.HoeffdingTree',
boosting_iterations: int = 100,
gamma=0.1,
)[source]#

OnlineSmoothBoost Classifier

Parameters:

  • schema – The schema of the stream.

  • random_seed – The random seed passed to the MOA learner.

  • base_learner – The base learner to be trained. Default trees.HoeffdingTree.

  • boosting_iterations – The number of boosting iterations (ensemble size).

  • gamma – The value of the gamma parameter.

cli_help()[source]#
predict(instance)[source]#

Predict the label of an instance.

The base implementation calls predict_proba() and returns the label with the highest probability.

Parameters:

instance – The instance to predict the label for.

Returns:

The predicted label or None if the classifier is unable to make a prediction.

predict_proba(instance)[source]#

Return probability estimates for each label.

Parameters:

instance – The instance to estimate the probabilities for.

Returns:

An array of probabilities for each label or None if the classifier is unable to make a prediction.

train(instance)[source]#

Train the classifier with a labeled instance.

Parameters:

instance – The labeled instance to train the classifier with.

random_seed: int#

The random seed for reproducibility.

When implementing a classifier ensure random number generators are seeded.

schema: Schema#

The schema representing the instances.