Package weka.classifiers.evaluation

Class CostCurve

java.lang.Object
weka.classifiers.evaluation.CostCurve
All Implemented Interfaces:
RevisionHandler
public class CostCurve extends Object implements RevisionHandler
Generates points illustrating probablity cost tradeoffs that can be obtained by varying the threshold value between classes. For example, the typical threshold value of 0.5 means the predicted probability of "positive" must be higher than 0.5 for the instance to be predicted as "positive".
Version:
$Revision: 15751 $
Author:
Mark Hall (mhall@cs.waikato.ac.nz)

  • Field Details

    • RELATION_NAME

      public static final String RELATION_NAME
      The name of the relation used in cost curve datasets
      See Also:

    • PROB_COST_FUNC_NAME

      public static final String PROB_COST_FUNC_NAME
      attribute name: Probability Cost Function
      See Also:

    • NORM_EXPECTED_COST_NAME

      public static final String NORM_EXPECTED_COST_NAME
      attribute name: Normalized Expected Cost
      See Also:

    • THRESHOLD_NAME

      public static final String THRESHOLD_NAME
      attribute name: Threshold
      See Also:

  • Constructor Details

    • CostCurve

      public CostCurve()

  • Method Details

    • getCurve

      public Instances getCurve(ArrayList<Prediction> predictions)
      Calculates the performance stats for the default class and return results as a set of Instances. The structure of these Instances is as follows:

      • Probability Cost Function
      • Normalized Expected Cost
      • Threshold contains the probability threshold that gives rise to the previous performance values.

      Parameters:
      predictions - the predictions to base the curve on
      Returns:
      datapoints as a set of instances, null if no predictions have been made.
      See Also:

    • getCurve

      public Instances getCurve(ArrayList<Prediction> predictions, int classIndex)
      Calculates the performance stats for the desired class and return results as a set of Instances.
      Parameters:
      predictions - the predictions to base the curve on
      classIndex - index of the class of interest.
      Returns:
      datapoints as a set of instances.

    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Returns:
      the revision