|  | ## CHAID

#### Synopsis

Learns a pruned decision tree based on a chi squared attribute relevance test.

#### Description

The CHAID decision tree learner works like the DecisionTreeLearner with one exception: it used a chi squared based criterion instead of the information gain or gain ratio criteria.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

- minimal size for split: The minimal size of a node in order to allow a split.
- minimal leaf size: The minimal size of all leaves.
- minimal gain: The minimal gain which must be achieved in order to produce a split.
- maximal depth: The maximum tree depth (-1: no bound)
- confidence: The confidence level used for the pessimistic error calculation of pruning.
- number of prepruning alternatives: The number of alternative nodes tried when prepruning would prevent a split.
- no pre pruning: Disables the pre pruning and delivers a tree without any prepruning.
- no pruning: Disables the pruning and delivers an unpruned tree.

#### ExampleProcess