SVM::crossvalidate

Test training params on subsets of the training data

&reftitle.description; public `float` `svm::crossvalidate` `array` `problem` `int` `number_of_folds`

Crossvalidate can be used to test the effectiveness of the current parameter set on a subset of the training data. Given a problem set and a n "folds", it separates the problem set into n subsets, and the repeatedly trains on one subset and tests on another. While the accuracy will generally be lower than a SVM trained on the enter data set, the accuracy score returned should be relatively useful, so it can be used to test different training parameters.

&reftitle.parameters;

`problem`

- The problem data. This can either be in the form of an array, the URL of an SVMLight formatted file, or a stream to an opened SVMLight formatted datasource.

`number_of_folds`

- The number of sets the data should be divided into and cross tested. A higher number means smaller training sets and less reliability. 5 is a good number to start with.

&reftitle.returnvalues;

The correct percentage, expressed as a floating point number from 0-1. In the case of NU_SVC or EPSILON_SVR kernels the mean squared error will returned instead.

&reftitle.seealso;

- `SVM::train`