Package 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 Details

    • updateStatsForClassifier

      void updateStatsForClassifier(double[] predictedDistribution, Instance instance) throws 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:
      Exception - if the class of the instance is not set

    • updateStatsForPredictor

      void updateStatsForPredictor(double predictedValue, Instance instance) throws 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:
      Exception - if the class of the instance is not set

    • updateStatsForConditionalDensityEstimator

      void updateStatsForConditionalDensityEstimator(ConditionalDensityEstimator classifier, Instance classMissing, double classValue) throws 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:
      Exception - if density could not be computed successfully

    • toSummaryString

      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