|  | ## Decision Stump

#### Synopsis

Learns only a root node of a decision tree. Can be very efficient when boosted.

#### Description

This operator learns decision stumps, i.e. a small decision tree with only one single split. This decision stump works on both numerical and nominal attributes.

#### 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