NominalLossFunction

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## weka.classifiers.misc.monotone
Interface NominalLossFunction

All Known Implementing Classes:
  AbsoluteLossFunction, ZeroOneLossFunction

    public interface NominalLossFunction

Interface for incorporating different loss functions.

This interface contains only one method, namely `loss` that measures the error between an actual class value `actual` and a predicted value `predicted.` It is understood that the return value of this method is always be positive and that it is zero if and only if the actual and the predicted value coincide.

This implementation is done as part of the master's thesis: "Studie en implementatie van instantie-gebaseerde algoritmen voor gesuperviseerd rangschikken", Stijn Lievens, Ghent University, 2004.

Version:
  $Revision: 1.1 $
Author:
  Stijn Lievens (stijn.lievens@ugent.be)

Method Summary
`double` | `loss(double actual, double predicted)` Calculate the loss between an actual and a predicted class value.

Method Detail

### loss

    double loss(double actual,
                double predicted)

  Calculate the loss between an actual and a predicted class value.

  Parameters:
    `actual` - the actual class value
    `predicted` - the predicted class value
  Returns:
    a measure for the error of making the prediction `predicted` instead of `actual`

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