|  | ## MetaCost

#### Synopsis

Builds a classification model using cost values from a given matrix.

#### Description

This operator uses a given cost matrix to compute label predictions according to classification costs. The method used by this operator is similar to MetaCost as described by Pedro Domingos.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- example set:

#### Parameters

- cost matrix: The cost matrix in Matlab single line format
- use subset for training: Fraction of examples used for training. Must be greater than 0 and should be lower than 1.
- iterations: The number of iterations (base models).
- sampling with replacement: Use sampling with replacement (true) or without (false)
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed

#### ExampleProcess