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Class weka.classifiers.evaluation.TwoClassStats
java.lang.Object
|
+----weka.classifiers.evaluation.TwoClassStats
- public class TwoClassStats
- extends java.lang.Object
Encapsulates performance functions for two-class problems.
- Version:
- $Revision: 1.5 $
- Author:
- Len Trigg (len@intelligenesis.net)
TwoClassStats(double, double, double, double)
- Creates the TwoClassStats with the given initial performance values.
getConfusionMatrix()
- Generates a
ConfusionMatrix
representing the current
two-class statistics, using class names "negative" and "positive".
getFallout()
- Calculate the fallout.
getFalseNegative()
- Gets the number of positive instances predicted as negative
getFalsePositive()
- Gets the number of negative instances predicted as positive
getFalsePositiveRate()
- Calculate the false positive rate.
getFMeasure()
- Calculate the F-Measure.
getPrecision()
- Calculate the precision.
getRecall()
- Calculate the recall.
getTrueNegative()
- Gets the number of negative instances predicted as negative
getTruePositive()
- Gets the number of positive instances predicted as positive
getTruePositiveRate()
- Calculate the true positive rate.
setFalseNegative(double)
- Sets the number of positive instances predicted as negative
setFalsePositive(double)
- Sets the number of negative instances predicted as positive
setTrueNegative(double)
- Sets the number of negative instances predicted as negative
setTruePositive(double)
- Sets the number of positive instances predicted as positive
toString()
- Returns a string containing the various performance measures
for the current object
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
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 as
correctly classified positives
------------------------------
total positives
- Returns:
- the true positive rate
getFalsePositiveRate
public double getFalsePositiveRate()
Calculate the false positive rate.
This is defined as
incorrectly classified negatives
--------------------------------
total negatives
- Returns:
- the false positive rate
getPrecision
public double getPrecision()
Calculate the precision.
This is defined as
correctly classified positives
------------------------------
total predicted as positive
- Returns:
- the precision
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
getFMeasure
public double getFMeasure()
Calculate the F-Measure.
This is defined as
2 * recall * precision
----------------------
recall + precision
- Returns:
- the F-Measure
getFallout
public double getFallout()
Calculate the fallout.
This is defined as
incorrectly classified negatives
--------------------------------
total predicted as positive
- Returns:
- the fallout
getConfusionMatrix
public ConfusionMatrix getConfusionMatrix()
Generates a ConfusionMatrix
representing the current
two-class statistics, using class names "negative" and "positive".
- Returns:
- a
ConfusionMatrix
.
toString
public java.lang.String toString()
Returns a string containing the various performance measures
for the current object
- Overrides:
- toString in class java.lang.Object
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