|  | ## Decision Tree (Weight-Based)

#### Synopsis

Learns a pruned decision tree based on an arbitrary feature relevance test (attribute weighting scheme as inner operator).

#### Description

Learns a pruned decision tree based on arbitrary feature relevance measurements defined by an inner operator (use for example InfoGainRatioWeighting for C4.5 and ChiSquaredWeighting for CHAID. Works only for nominal attributes.

#### Input

- training set: expects: ExampleSet

#### Output

- model:

#### 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.
- maximal depth: The maximum tree depth (-1: no bound)
- confidence: The confidence level used for pruning.
- no pruning: Disables the pruning and delivers an unpruned tree.
- number of prepruning alternatives: The number of alternative nodes tried when prepruning would prevent a split.

#### ExampleProcess