HoeffdingAdaptiveTree#

class capymoa.classifier.HoeffdingAdaptiveTree[source]#

Bases: HoeffdingTree

Hoeffding Adaptive Tree (HAT) classifier.

Reference:

Bifet, A. and Gavalda, R., 2009. Adaptive learning from evolving data streams. In Advances in Intelligent Data Analysis VIII: 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31-September 2, 2009. Proceedings 8 (pp. 249-260). Springer Berlin Heidelberg. https://link.springer.com/chapter/10.1007/978-3-642-03915-7_22

>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.classifier import HoeffdingAdaptiveTree
>>> from capymoa.evaluation import prequential_evaluation
>>> stream = ElectricityTiny()
>>> schema = stream.get_schema()
>>> learner = HoeffdingAdaptiveTree(schema)
>>> results = prequential_evaluation(stream, learner, max_instances=1000)
>>> results["cumulative"].accuracy()
84.1
__init__(
schema: Schema,
random_seed: int = 0,
grace_period: int = 200,
split_criterion: str | SplitCriterion = 'InfoGainSplitCriterion',
confidence: float = 0.001,
tie_threshold: float = 0.05,
leaf_prediction: int = 'NaiveBayesAdaptive',
nb_threshold: int = 0,
numeric_attribute_observer: str = 'GaussianNumericAttributeClassObserver',
binary_split: bool = False,
max_byte_size: float = 33554433,
memory_estimate_period: int = 1000000,
stop_mem_management: bool = True,
remove_poor_attrs: bool = False,
disable_prepruning: bool = True,
)[source]#

Hoeffding Adaptive Tree (HAT) classifier.

Parameters:

  • schema – the schema of the stream.

  • random_seed – the random seed passed to the moa learner.

  • grace_period – the number of instances a leaf should observe between split attempts.

  • split_criterion – the split criterion to use. Defaults to InfoGainSplitCriterion.

  • confidence – the confidence level to calculate the Hoeffding Bound (1 - delta). Defaults to 1e-3. Values closer to zero imply longer split decision delays.

  • tie_threshold – the threshold below which a split will be forced to break ties.

  • leaf_prediction – the Prediction mechanism used at leafs.</br> - 0 - Majority Class</br> - 1 - Naive Bayes</br> - 2 - Naive Bayes Adaptive</br>

  • nb_threshold – the number of instances a leaf should observe before allowing Naive Bayes.

  • numeric_attribute_observer – the Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits.

  • binary_split – If True, only allow binary splits.

  • max_byte_size – the max size of the tree, in bytes.

  • memory_estimate_period – Interval (number of processed instances) between memory consumption checks.

  • stop_mem_management – If True, stop growing as soon as memory limit is hit.

  • remove_poor_attrs – If True, disable poor attributes to reduce memory usage.

  • disable_prepruning – If True, disable merit-based tree pre-pruning.

CLI_help()[source]#
predict(instance)[source]#
predict_proba(instance)[source]#
train(instance)[source]#