EFDT#

class capymoa.classifier.EFDT[source]#

Bases: MOAClassifier

Extremely Fast Decision Tree (EFDT) Classifier

Also referred to as the Hoeffding AnyTime Tree (HATT) classifier. In practice, despite the name, EFDTs are typically slower than a vanilla Hoeffding Tree to process data. The speed differences come from the mechanism of split re-evaluation present in EFDT. Nonetheless, EFDT has theoretical properties that ensure it converges faster than the vanilla Hoeffding Tree to the structure that would be created by a batch decision tree model (such as Classification and Regression Trees - CART). Keep in mind that such propositions hold when processing a stationary data stream. When dealing with non-stationary data, EFDT is somewhat robust to concept drifts as it continually revisits and updates its internal decision tree structure. Still, in such cases, the Hoeffding Adaptive Tree might be a better option, as it was specifically designed to handle non-stationarity.

Reference:

Extremely fast decision tree. Manapragada, Chaitanya, G. I. Webb, M. Salehi. ACM SIGKDD, pp. 1953-1962, 2018.

Example usage:

>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.classifier import EFDT
>>> from capymoa.evaluation import prequential_evaluation
>>> stream = ElectricityTiny()
>>> schema = stream.get_schema()
>>> learner = EFDT(schema)
>>> results = prequential_evaluation(stream, learner, max_instances=1000)
>>> results["cumulative"].accuracy()
84.39999999999999
__init__(
schema: Schema | None = None,
random_seed: int = 0,
grace_period: int = 200,
min_samples_reevaluate: int = 200,
split_criterion: str | SplitCriterion = 'InfoGainSplitCriterion',
confidence: float = 0.001,
tie_threshold: float = 0.05,
leaf_prediction: str = '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]#

Construct an Extremely Fast Decision Tree (EFDT) Classifier

Parameters:

  • schema – The schema of the stream.

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

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

  • min_samples_reevaluate – Number of instances a node should observe before re-evaluating the best split.

  • split_criterion – Split criterion to use. Defaults to InfoGainSplitCriterion.

  • confidence – Significance level to calculate the Hoeffding bound. The significance level is given by 1 - delta. Values closer to zero imply longer split decision delays.

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

  • leaf_prediction – Prediction mechanism used at the leaves (“MajorityClass” or 0, “NaiveBayes” or 1, “NaiveBayesAdaptive” or 2).

  • nb_threshold – 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]#