SVM::crossvalidate
(PECL svm >= 0.1.0)
SVM::crossvalidate — Test training params on subsets of the training data
Description
public function svm::crossvalidate(
array $problem,
int $number_of_folds):
float
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 entire data set, the
accuracy score returned should be relatively useful, so it can be used to
test different training parameters.
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.
Return Values
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.
SVM::__construct
SVM