|  | ## ID3

#### Synopsis

Learns an unpruned decision tree from nominal attributes only.

#### Description

This operator learns decision trees without pruning using nominal attributes only. Decision trees are powerful classification methods which often can also easily be understood. This decision tree learner works similar to Quinlan's ID3.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

- criterion: Specifies the used criterion for selecting attributes and numerical splits.
- 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.

#### ExampleProcess