Package weka.classifiers.evaluation
Class TwoClassStats
java.lang.Object
weka.classifiers.evaluation.TwoClassStats
- All Implemented Interfaces:
RevisionHandler
Encapsulates performance functions for two-class problems.
- Version:
- $Revision: 14382 $
- Author:
- Len Trigg (len@reeltwo.com)
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Constructor Summary
ConstructorsConstructorDescriptionTwoClassStats(double tp, double fp, double tn, double fn) Creates the TwoClassStats with the given initial performance values. -
Method Summary
Modifier and TypeMethodDescriptionGenerates aConfusionMatrixrepresenting the current two-class statistics, using class names "negative" and "positive".doubleCalculate the fallout.doubleGets the number of positive instances predicted as negativedoubleGets the number of negative instances predicted as positivedoubleCalculate the false positive rate.doubleCalculate the F-Measure.doubleCalculate the precision.doubleCalculate the recall.Returns the revision string.doubleGets the number of negative instances predicted as negativedoubleGets the number of positive instances predicted as positivedoubleCalculate the true positive rate.voidsetFalseNegative(double fn) Sets the number of positive instances predicted as negativevoidsetFalsePositive(double fp) Sets the number of negative instances predicted as positivevoidsetTrueNegative(double tn) Sets the number of negative instances predicted as negativevoidsetTruePositive(double tp) Sets the number of positive instances predicted as positivetoString()Returns a string containing the various performance measures for the current object
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Constructor Details
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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 Details
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setTruePositive
public void setTruePositive(double tp) Sets the number of positive instances predicted as positive -
setFalsePositive
public void setFalsePositive(double fp) Sets the number of negative instances predicted as positive -
setTrueNegative
public void setTrueNegative(double tn) Sets the number of negative instances predicted as negative -
setFalseNegative
public void setFalseNegative(double fn) Sets the number of positive instances predicted as negative -
getTruePositive
public double getTruePositive()Gets the number of positive instances predicted as positive -
getFalsePositive
public double getFalsePositive()Gets the number of negative instances predicted as positive -
getTrueNegative
public double getTrueNegative()Gets the number of negative instances predicted as negative -
getFalseNegative
public double getFalseNegative()Gets the number of positive instances predicted as negative -
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
Generates aConfusionMatrixrepresenting the current two-class statistics, using class names "negative" and "positive".- Returns:
- a
ConfusionMatrix.
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toString
Returns a string containing the various performance measures for the current object -
getRevision
Returns the revision string.- Specified by:
getRevisionin interfaceRevisionHandler- Returns:
- the revision
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