|  | ## Create Learning Curve

#### Synopsis

Iterates its inner operator for an increasing number of samples and collects the performances.

#### Description

This operator first divides the input example set into two parts, a training set and a test set according to the parameter "training_ratio". It then uses iteratively bigger subsets from the fixed training set for learning (the first subprocess) and calculates the corresponding performance values on the fixed test set (with the second subprocess).

#### Input

- exampleSet: expects: ExampleSet

#### Output

#### Parameters

- training ratio: The fraction of examples which shall be maximal used for training (dynamically growing), the rest is used for testing (fixed)
- step fraction: The fraction of examples which would be additionally used in each step.
- start fraction: Starts with this fraction of the training data and iteratively add step_fraction examples from the training data.
- sampling type: Defines the sampling type of the cross validation (linear = consecutive subsets, shuffled = random subsets, stratified = random subsets with class distribution kept constant)
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed

#### ExampleProcess