|  | ## Wrapper-X-Validation

#### Synopsis

Encapsulates a cross-validation to evaluate a feature weighting or selection method (wrapper).

#### Description

This operator evaluates the performance of feature weighting and selection algorithms. The first subprocess contains the algorithm to be evaluated itself. It must return an attribute weights vector which is then applied on the test data. The same fold XValidation of the data is used to create a new model during the second subprocess. This model is evaluated in the third subprocess which hence has to return a performance vector. This performance vector serves as a performance indicator for the actual algorithm. This implementation of a MethodValidationChain works similar to the XValidation.

#### Input

- example set in:

#### Output

- performance vector out:
- attribute weights out:

#### Parameters

- leave one out: Set the number of validations to the number of examples. If set to true, number_of_validations is ignored
- number of validations: Number of subsets for the crossvalidation
- 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