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## weka.classifiers.evaluation
Class TwoClassStats

    java.lang.Object
      weka.classifiers.evaluation.TwoClassStats

    public class TwoClassStats

extends java.lang.Object

Encapsulates performance functions for two-class problems.

Version:
  $Revision: 1.7 $
Author:
  Len Trigg (len@reeltwo.com)

Constructor Summary
`TwoClassStats(double tp, double fp, double tn, double fn)` Creates the TwoClassStats with the given initial performance values.

Method Summary
`ConfusionMatrix` | `getConfusionMatrix()` Generates a `ConfusionMatrix` 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 negative
`double` | `getFalsePositive()` Gets the number of negative instances predicted as positive
`double` | `getFalsePositiveRate()` Calculate the false positive rate.
`double` | `getFMeasure()` Calculate the F-Measure.
`double` | `getPrecision()` Calculate the precision.
`double` | `getRecall()` Calculate the recall.
`double` | `getTrueNegative()` Gets the number of negative instances predicted as negative
`double` | `getTruePositive()` Gets the number of positive instances predicted as positive
`double` | `getTruePositiveRate()` Calculate the true positive rate.
`void` | `setFalseNegative(double fn)` Sets the number of positive instances predicted as negative
`void` | `setFalsePositive(double fp)` Sets the number of negative instances predicted as positive
`void` | `setTrueNegative(double tn)` Sets the number of negative instances predicted as negative
`void` | `setTruePositive(double tp)` Sets the number of positive instances predicted as positive
`java.lang.String` | `toString()` Returns a string containing the various performance measures for the current object

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

Constructor Detail

### 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

Method Detail

### 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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