|  | ## Logistic Regression (Evolutionary)

#### Synopsis

A kernel logistic regression learner for binary classification tasks.

#### Description

This operator determines a logistic regression model.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

- kernel type: The kernel type
- kernel gamma: The kernel parameter gamma.
- kernel sigma1: The kernel parameter sigma1.
- kernel sigma2: The kernel parameter sigma2.
- kernel sigma3: The kernel parameter sigma3.
- kernel degree: The kernel parameter degree.
- kernel shift: The kernel parameter shift.
- kernel a: The kernel parameter a.
- kernel b: The kernel parameter b.
- C: The complexity constant.
- start population type: The type of start population initialization.
- max generations: Stop after this many evaluations
- generations without improval: Stop after this number of generations without improvement (-1: optimize until max_iterations).
- population size: The population size (-1: number of examples)
- tournament fraction: The fraction of the population used for tournament selection.
- keep best: Indicates if the best individual should survive (elititst selection).
- mutation type: The type of the mutation operator.
- selection type: The type of the selection operator.
- crossover prob: The probability for crossovers.
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed
- show convergence plot: Indicates if a dialog with a convergence plot should be drawn.

#### ExampleProcess