Class CrossValidator<S extends Observation,​T extends Category>

  • 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 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