HoeffdingTree#

class capymoa.classifier.HoeffdingTree[source]#

Bases: MOAClassifier

Hoeffding Tree 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.

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 leafs.</br> - 0 - Majority Class</br> - 1 - Naive Bayes</br> - 2 - Naive Bayes Adaptive</br>

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.

__init__(
schema: Schema | None = None,
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]#
CLI_help()[source]#
predict(instance)[source]#
predict_proba(instance)[source]#
train(instance)[source]#