OnlineSmoothBoost

class capymoa.classifier.OnlineSmoothBoost

Bases: MOAClassifier

OnlineSmoothBoost Classifier

Incremental on-line boosting with Theoretical Justifications of Shang-Tse Chen

Reference:

An Online Boosting Algorithm with Theoretical Justifications. Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu. ICML, 2012.

Example usages:

>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.classifier import OnlineSmoothBoost
>>> from capymoa.evaluation import prequential_evaluation
>>> stream = ElectricityTiny()
>>> schema = stream.get_schema()
>>> learner = OnlineSmoothBoost(schema)
>>> results = prequential_evaluation(stream, learner, max_instances=1000)
>>> results["cumulative"].accuracy()
87.8

__init__(

schema: Schema | None = None,

random_seed: int = 0,

base_learner='trees.HoeffdingTree',

boosting_iterations: int = 100,

gamma=0.1,

)

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()

predict(instance)

predict_proba(instance)

train(instance)