net.sf.tweety.machinelearning

Class CrossValidator<S extends Observation,T extends Category>

java.lang.Object

net.sf.tweety.machinelearning.ClassificationTester<S,T>

net.sf.tweety.machinelearning.CrossValidator<S,T>

Type Parameters:

S - the type of observation

T - the type of category

public class CrossValidator<S extends Observation,T extends Category>
extends ClassificationTester<S,T>

Performs cross-validation in a classifier, i.e. it divides a given training set into N parts (more precisely, for each category present in the training set, the observations belonging each category are partitioned into N parts), for each i=1,...,N trains a classifier on the union of all parts except i, and measures the performance on part i. The final performance measure is the average on these N rounds.

Author:

Matthias Thimm

Field Summary

Fields

Modifier and Type	Field and Description
private int	fold The number of partitions for cross-validation.

Constructor Summary

Constructors

Constructor and Description
CrossValidator(int fold) Creates a new cross-validator with the given number of partitions.

Method Summary

Modifier and Type	Method and Description
double	test(Trainer<S,T> trainer, TrainingSet<S,T> trainingSet) This methods takes a trainer and a training set and returns the performance (in [0,1]) of the trained classifier on the training set (e.g.

Methods inherited from class net.sf.tweety.machinelearning.ClassificationTester

test

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

fold

private int fold

The number of partitions for cross-validation.

Constructor Detail

CrossValidator

public CrossValidator(int fold)

Creates a new cross-validator with the given number of partitions.

Parameters:

fold - the number of partitions.

Method Detail

test

public double test(Trainer<S,T> trainer,
                   TrainingSet<S,T> trainingSet)

Description copied from class: ClassificationTester

This methods takes a trainer and a training set and returns the performance (in [0,1]) of the trained classifier on the training set (e.g. using cross-validation). The larger the value the better the trained classifier.

Specified by:

test in class ClassificationTester<S extends Observation,T extends Category>

Parameters:

trainer - some trainer

trainingSet - some training set

Returns:

the performance of the trained classifier