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Class weka.classifiers.evaluation.TwoClassStats

java.lang.Object
   |
   +----weka.classifiers.evaluation.TwoClassStats

public class TwoClassStats
extends Object
Encapsulates performance functions for two-class problems.

Author:
Len Trigg (len@intelligenesis.net)

Constructor Index

 o TwoClassStats(double, double, double, double)
Creates the TwoClassStats with the given initial performance values.

Method Index

 o getConfusionMatrix()
Generates a ConfusionMatrix representing the current two-class statistics, using class names "negative" and "positive".
 o getFallout()
Calculate the fallout.
 o getFalseNegative()
Gets the number of positive instances predicted as negative
 o getFalsePositive()
Gets the number of negative instances predicted as positive
 o getFalsePositiveRate()
Calculate the false positive rate.
 o getFMeasure()
Calculate the F-Measure.
 o getPrecision()
Calculate the precision.
 o getRecall()
Calculate the recall.
 o getTrueNegative()
Gets the number of negative instances predicted as negative
 o getTruePositive()
Gets the number of positive instances predicted as positive
 o getTruePositiveRate()
Calculate the true positive rate.
 o setFalseNegative(double)
Sets the number of positive instances predicted as negative
 o setFalsePositive(double)
Sets the number of negative instances predicted as positive
 o setTrueNegative(double)
Sets the number of negative instances predicted as negative
 o setTruePositive(double)
Sets the number of positive instances predicted as positive
 o toString()
Returns a string containing the various performance measures for the current object

Constructors

 o TwoClassStats
 public TwoClassStats(double tp,
                      double fp,
                      double tn,
                      double fn)
Creates the TwoClassStats with the given initial performance values.

Parameters:
tp - the number of correctly classified positives
fp - the number of incorrectly classified negatives
tn - the number of correctly classified negatives
fn - the number of incorrectly classified positives

Methods

 o setTruePositive
 public void setTruePositive(double tp)
Sets the number of positive instances predicted as positive

 o setFalsePositive
 public void setFalsePositive(double fp)
Sets the number of negative instances predicted as positive

 o setTrueNegative
 public void setTrueNegative(double tn)
Sets the number of negative instances predicted as negative

 o setFalseNegative
 public void setFalseNegative(double fn)
Sets the number of positive instances predicted as negative

 o getTruePositive
 public double getTruePositive()
Gets the number of positive instances predicted as positive

 o getFalsePositive
 public double getFalsePositive()
Gets the number of negative instances predicted as positive

 o getTrueNegative
 public double getTrueNegative()
Gets the number of negative instances predicted as negative

 o getFalseNegative
 public double getFalseNegative()
Gets the number of positive instances predicted as negative

 o getTruePositiveRate
 public double getTruePositiveRate()
Calculate the true positive rate. This is defined as

 correctly classified positives
 ------------------------------
       total positives
 

Returns:
the true positive rate
 o getFalsePositiveRate
 public double getFalsePositiveRate()
Calculate the false positive rate. This is defined as

 incorrectly classified negatives
 --------------------------------
        total negatives
 

Returns:
the false positive rate
 o getPrecision
 public double getPrecision()
Calculate the precision. This is defined as

 correctly classified positives
 ------------------------------
  total predicted as positive
 

Returns:
the precision
 o getRecall
 public double getRecall()
Calculate the recall. This is defined as

 correctly classified positives
 ------------------------------
       total positives
 

(Which is also the same as the truePositiveRate.)

Returns:
the recall
 o getFMeasure
 public double getFMeasure()
Calculate the F-Measure. This is defined as

 2 * recall * precision
 ----------------------
   recall + precision
 

Returns:
the F-Measure
 o getFallout
 public double getFallout()
Calculate the fallout. This is defined as

 incorrectly classified negatives
 --------------------------------
   total predicted as positive
 

Returns:
the fallout
 o getConfusionMatrix
 public ConfusionMatrix getConfusionMatrix()
Generates a ConfusionMatrix representing the current two-class statistics, using class names "negative" and "positive".

Returns:
a ConfusionMatrix.
 o toString
 public String toString()
Returns a string containing the various performance measures for the current object

Overrides:
toString in class Object

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