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