CrossValidator

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

Constructor Summary

Constructors
Constructor Description

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

Method Summary

Modifier and Type	Method	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
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- 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