HistogramSplitter

Numeric attribute observer for classification tasks that discretizes features using histograms.

Parameters

• n_bins (int) – defaults to 256

  The maximum number of bins in the histogram.

• n_splits (int) – defaults to 32

  The number of split points to evaluate when querying for the best split candidate.

Attributes

• is_numeric

  Determine whether or not the splitter works with numerical features.

• is_target_class

  Check on which kind of learning task the splitter is designed to work. If True, the splitter works with classification trees, otherwise it is designed for regression trees.

Methods

best_evaluated_split_suggestion

Get the best split suggestion given a criterion and the target's statistics.

Parameters

• criterion (river.tree.split_criterion.base.SplitCriterion)
• pre_split_dist (Union[List, Dict])
• att_idx (Hashable)
• binary_only (bool)

Returns

BranchFactory: Suggestion of the best attribute split.

cond_proba

Get the probability for an attribute value given a class.

Parameters

• att_val
• target_val (Union[bool, str, int])

Returns

float: Probability for an attribute value given a class.

update

Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.

Parameters

• att_val
• target_val (Union[bool, str, int, numbers.Number])
• sample_weight (float)