Skip to content

Splitter

Base class for the tree splitters.

Each Attribute Observer (AO) or Splitter monitors one input feature and finds the best split point for this attribute. AOs can also perform other tasks related to the monitored feature, such as estimating its probability density function (classification case).

This class should not be instantiated, as none of its methods are implemented.

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 — 'SplitCriterion'
  • pre_split_dist — 'list | dict'
  • att_idx — 'base.typing.FeatureName'
  • 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 — 'base.typing.ClfTarget'

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 — 'base.typing.Target'
  • sample_weight — 'float'