ClassificationTester

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 Description

ClassificationTester()

Method Summary

Modifier and Type	Method	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
equals, 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.
    
    Returns:
    
    the performance of the given classifier