|  | ## Random Forest

#### Synopsis

Learns a set of random trees, i.e. for each split only a random subset of attributes is available. The resulting model is a voting model of all trees.

#### Description

This operators learns a random forest. The resulting forest model contains serveral single random tree models.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

- number of trees: The number of learned random trees.
- 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.
- 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.
- guess subset ratio: Indicates that log(m) + 1 features are used, otherwise a ratio has to be specified.
- subset ratio: Ratio of randomly chosen attributes to test
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed

#### ExampleProcess