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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)
-
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 String toString()
- Returns a string containing the various performance measures
for the current object
- Overrides:
- toString in class Object
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