|  | ## Compare ROCs

#### Synopsis

Generates a ROC chart for the models created by each of the inner learners and plot all charts in the same plotter.

#### Description

This operator uses its inner operators (each of those must produce a model) and calculates the ROC curve for each of them. All ROC curves together are plotted in the same plotter. The comparison is based on the average values of a k-fold cross validation. Alternatively, this operator can use an internal split into a test and a training set from the given data set. Please note that a former predicted label of the given example set will be removed during the application of this operator.

#### Input

- example set: expects: ExampleSet

#### Output

- exampleSet:
- rocComparison:

#### Parameters

- number of folds: The number of folds used for a cross validation evaluation (-1: use simple split ratio).
- split ratio: Relative size of the training set
- 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
- use example weights: Indicates if example weights should be regarded (use weight 1 for each example otherwise).
- roc bias: Determines how the ROC (and AUC) are evaluated: Count correct predictions first, last, or alternatingly

#### ExampleProcess