net.sf.tweety.machinelearning

Class ClassificationTester<S extends Observation,T extends Category>

java.lang.Object

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

Type Parameters:

S - type of observations

T - type of categories

Direct Known Subclasses:

CrossValidator

public abstract class ClassificationTester<S extends Observation,T extends Category>
extends java.lang.Object

Classes implementing this interface provide the means to test a training mechanism for performance.

Author:

Matthias Thimm

Constructor Summary

Constructors

Constructor and Description
ClassificationTester()

Method Summary

Modifier and Type	Method and Description
double	test(Classifier classifier, TrainingSet<S,T> trainingSet) Measures the performance of the given classifier on the given test set, i.e.
abstract 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 java.lang.Object

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

Constructor Detail

ClassificationTester

public ClassificationTester()

Method Detail

test

public abstract 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. using cross-validation). The larger the value the better the trained classifier.

Parameters:

trainer - some trainer

trainingSet - some training set

Returns:

the performance of the trained classifier

test

public double test(Classifier classifier,
                   TrainingSet<S,T> trainingSet)

Measures the performance of the given classifier on the given test set, i.e. every observation from the training set is classified by the classifier and its prediction is compared with the provided category. The return value is the ratio of the correctly classified observations and the total number of observations.

Parameters:

classifier - some classifier.

trainingSet - some training set.