Package weka.classifiers.evaluation
Class TwoClassStats
- java.lang.Object
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- weka.classifiers.evaluation.TwoClassStats
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- All Implemented Interfaces:
RevisionHandler
public class TwoClassStats extends java.lang.Object implements RevisionHandler
Encapsulates performance functions for two-class problems.- Version:
- $Revision: 1.9 $
- Author:
- Len Trigg (len@reeltwo.com)
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Constructor Summary
Constructors Constructor Description TwoClassStats(double tp, double fp, double tn, double fn)
Creates the TwoClassStats with the given initial performance values.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description ConfusionMatrix
getConfusionMatrix()
Generates aConfusionMatrix
representing the current two-class statistics, using class names "negative" and "positive".double
getFallout()
Calculate the fallout.double
getFalseNegative()
Gets the number of positive instances predicted as negativedouble
getFalsePositive()
Gets the number of negative instances predicted as positivedouble
getFalsePositiveRate()
Calculate the false positive rate.double
getFMeasure()
Calculate the F-Measure.double
getPrecision()
Calculate the precision.double
getRecall()
Calculate the recall.java.lang.String
getRevision()
Returns the revision string.double
getTrueNegative()
Gets the number of negative instances predicted as negativedouble
getTruePositive()
Gets the number of positive instances predicted as positivedouble
getTruePositiveRate()
Calculate the true positive rate.void
setFalseNegative(double fn)
Sets the number of positive instances predicted as negativevoid
setFalsePositive(double fp)
Sets the number of negative instances predicted as positivevoid
setTrueNegative(double tn)
Sets the number of negative instances predicted as negativevoid
setTruePositive(double tp)
Sets the number of positive instances predicted as positivejava.lang.String
toString()
Returns a string containing the various performance measures for the current object
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Constructor Detail
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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 positivesfp
- the number of incorrectly classified negativestn
- the number of correctly classified negativesfn
- the number of incorrectly classified positives
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Method Detail
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setTruePositive
public void setTruePositive(double tp)
Sets the number of positive instances predicted as positive
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setFalsePositive
public void setFalsePositive(double fp)
Sets the number of negative instances predicted as positive
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setTrueNegative
public void setTrueNegative(double tn)
Sets the number of negative instances predicted as negative
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setFalseNegative
public void setFalseNegative(double fn)
Sets the number of positive instances predicted as negative
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getTruePositive
public double getTruePositive()
Gets the number of positive instances predicted as positive
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getFalsePositive
public double getFalsePositive()
Gets the number of negative instances predicted as positive
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getTrueNegative
public double getTrueNegative()
Gets the number of negative instances predicted as negative
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getFalseNegative
public double getFalseNegative()
Gets the number of positive instances predicted as negative
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getTruePositiveRate
public double getTruePositiveRate()
Calculate the true positive rate. This is defined ascorrectly classified positives ------------------------------ total positives
- Returns:
- the true positive rate
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getFalsePositiveRate
public double getFalsePositiveRate()
Calculate the false positive rate. This is defined asincorrectly classified negatives -------------------------------- total negatives
- Returns:
- the false positive rate
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getPrecision
public double getPrecision()
Calculate the precision. This is defined ascorrectly classified positives ------------------------------ total predicted as positive
- Returns:
- the precision
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getRecall
public double getRecall()
Calculate the recall. This is defined ascorrectly classified positives ------------------------------ total positives
(Which is also the same as the truePositiveRate.)
- Returns:
- the recall
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getFMeasure
public double getFMeasure()
Calculate the F-Measure. This is defined as2 * recall * precision ---------------------- recall + precision
- Returns:
- the F-Measure
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getFallout
public double getFallout()
Calculate the fallout. This is defined asincorrectly classified negatives -------------------------------- total predicted as positive
- Returns:
- the fallout
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getConfusionMatrix
public ConfusionMatrix getConfusionMatrix()
Generates aConfusionMatrix
representing the current two-class statistics, using class names "negative" and "positive".- Returns:
- a
ConfusionMatrix
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toString
public java.lang.String toString()
Returns a string containing the various performance measures for the current object- Overrides:
toString
in classjava.lang.Object
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Returns:
- the revision
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