weka.classifiers.evaluation

## Interface InformationTheoreticEvaluationMetric

-     public interface InformationTheoreticEvaluationMetric

  Primarily a marker interface for information theoretic evaluation metrics to implement. Allows the command line interface to display these metrics or not based on user-supplied options
  Version:
    $Revision: 9320 $
  Author:
    Mark Hall (mhall{[at]}pentaho{[dot]}com)

- - ### Method Summary

    All Methods Instance Methods Abstract Methods
    Modifier and Type | Method and Description
    --- | ---
    `java.lang.String` | `toSummaryString()` Return a formatted string (suitable for displaying in console or GUI output) containing all the statistics that this metric computes.
    `void` | `updateStatsForClassifier(double[] predictedDistribution, Instance instance)` Updates the statistics about a classifiers performance for the current test instance.
    `void` | `updateStatsForConditionalDensityEstimator(ConditionalDensityEstimator classifier, Instance classMissing, double classValue)` Updates stats for conditional density estimator based on current test instance.
    `void` | `updateStatsForPredictor(double predictedValue, Instance instance)` Updates the statistics about a predictors performance for the current test instance.

- - ### Method Detail

    - #### updateStatsForClassifier

          void updateStatsForClassifier(double[] predictedDistribution,
                                        Instance instance)
                                 throws java.lang.Exception

      Updates the statistics about a classifiers performance for the current test instance. Gets called when the class is nominal. Implementers need only implement this method if it is not possible to compute their statistics from what is stored in the base Evaluation object.
      Parameters:
        `predictedDistribution` - the probabilities assigned to each class
        `instance` - the instance to be classified
      Throws:
        `java.lang.Exception` - if the class of the instance is not set

    - #### updateStatsForPredictor

          void updateStatsForPredictor(double predictedValue,
                                       Instance instance)
                                throws java.lang.Exception

      Updates the statistics about a predictors performance for the current test instance. Gets called when the class is numeric. Implementers need only implement this method if it is not possible to compute their statistics from what is stored in the base Evaluation object.
      Parameters:
        `predictedValue` - the numeric value the classifier predicts
        `instance` - the instance to be classified
      Throws:
        `java.lang.Exception` - if the class of the instance is not set

    - #### updateStatsForConditionalDensityEstimator

          void updateStatsForConditionalDensityEstimator(ConditionalDensityEstimator classifier,
                                                         Instance classMissing,
                                                         double classValue)
                                                  throws java.lang.Exception

      Updates stats for conditional density estimator based on current test instance. Gets called when the class is numeric and the classifier is a ConditionalDensityEstimators. Implementers need only implement this method if it is not possible to compute their statistics from what is stored in the base Evaluation object.
      Parameters:
        `classifier` - the conditional density estimator
        `classMissing` - the instance for which density is to be computed, without a class value
        `classValue` - the class value of this instance
      Throws:
        `java.lang.Exception` - if density could not be computed successfully

    - #### toSummaryString

          java.lang.String toSummaryString()

      Return a formatted string (suitable for displaying in console or GUI output) containing all the statistics that this metric computes.
      Returns:
        a formatted string containing all the computed statistics