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## weka.classifiers.evaluation
Class ConfusionMatrix

    java.lang.Object
      weka.core.Matrix
          weka.classifiers.evaluation.ConfusionMatrix

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable

    public class ConfusionMatrix

extends Matrix

Cells of this matrix correspond to counts of the number (or weight) of predictions for each actual value / predicted value combination.

Version:
  $Revision: 1.5.2.1 $
Author:
  Len Trigg (len@reeltwo.com)
See Also:
  Serialized Form

Constructor Summary
`ConfusionMatrix(java.lang.String[] classNames)` Creates the confusion matrix with the given class names.

Method Summary
`void` | `addPrediction(NominalPrediction pred)` Includes a prediction in the confusion matrix.
`void` | `addPredictions(FastVector predictions)` Includes a whole bunch of predictions in the confusion matrix.
`java.lang.String` | `className(int index)` Gets the name of one of the classes.
`java.lang.Object` | `clone()` Creates and returns a clone of this object.
`double` | `correct()` Gets the number of correct classifications (that is, for which a correct prediction was made).
`double` | `errorRate()` Returns the estimated error rate.
`TwoClassStats` | `getTwoClassStats(int classIndex)` Gets the performance with respect to one of the classes as a TwoClassStats object.
`double` | `incorrect()` Gets the number of incorrect classifications (that is, for which an incorrect prediction was made).
`ConfusionMatrix` | `makeWeighted(CostMatrix costs)` Makes a copy of this ConfusionMatrix after applying the supplied CostMatrix to the cells.
`int` | `size()` Gets the number of classes.
`java.lang.String` | `toString()` Calls toString() with a default title.
`java.lang.String` | `toString(java.lang.String title)` Outputs the performance statistics as a classification confusion matrix.
`double` | `total()` Gets the number of predictions that were made (actually the sum of the weights of predictions where the class value was known).

Methods inherited from class weka.core.Matrix
`add, addElement, eigenvalueDecomposition, getColumn, getElement, getL, getRow, getU, isSymmetric, LUDecomposition, main, multiply, numColumns, numRows, parseMatlab, regression, regression, setColumn, setElement, setRow, solve, toMatlab, transpose, write`

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

Constructor Detail

### ConfusionMatrix

    public ConfusionMatrix(java.lang.String[] classNames)

  Creates the confusion matrix with the given class names.

  Parameters:
    `classNames` - an array containing the names the classes.

Method Detail

### makeWeighted

    public ConfusionMatrix makeWeighted(CostMatrix costs)
                                 throws java.lang.Exception

  Makes a copy of this ConfusionMatrix after applying the supplied CostMatrix to the cells. The resulting ConfusionMatrix can be used to get cost-weighted statistics.

  Parameters:
    `costs` - the CostMatrix.
  Returns:
    a ConfusionMatrix that has had costs applied.
  Throws:
    `java.lang.Exception` - if the CostMatrix is not of the same size as this ConfusionMatrix.

### clone

    public java.lang.Object clone()

  Creates and returns a clone of this object.

  Overrides:
    `clone` in class `Matrix`
  Returns:
    a clone of this instance.

### size

    public int size()

  Gets the number of classes.

  Returns:
    the number of classes

### className

    public java.lang.String className(int index)

  Gets the name of one of the classes.

  Parameters:
    `index` - the index of the class.
  Returns:
    the class name.

### addPrediction

    public void addPrediction(NominalPrediction pred)
                       throws java.lang.Exception

  Includes a prediction in the confusion matrix.

  Parameters:
    `pred` - the NominalPrediction to include
  Throws:
    `java.lang.Exception` - if no valid prediction was made (i.e. unclassified).

### addPredictions

    public void addPredictions(FastVector predictions)
                        throws java.lang.Exception

  Includes a whole bunch of predictions in the confusion matrix.

  Parameters:
    `predictions` - a FastVector containing the NominalPredictions to include
  Throws:
    `java.lang.Exception` - if no valid prediction was made (i.e. unclassified).

### getTwoClassStats

    public TwoClassStats getTwoClassStats(int classIndex)

  Gets the performance with respect to one of the classes as a TwoClassStats object.

  Parameters:
    `classIndex` - the index of the class of interest.
  Returns:
    the generated TwoClassStats object.

### correct

    public double correct()

  Gets the number of correct classifications (that is, for which a correct prediction was made). (Actually the sum of the weights of these classifications)

  Returns:
    the number of correct classifications

### incorrect

    public double incorrect()

  Gets the number of incorrect classifications (that is, for which an incorrect prediction was made). (Actually the sum of the weights of these classifications)

  Returns:
    the number of incorrect classifications

### total

    public double total()

  Gets the number of predictions that were made (actually the sum of the weights of predictions where the class value was known).

  Returns:
    the number of predictions with known class

### errorRate

    public double errorRate()

  Returns the estimated error rate.

  Returns:
    the estimated error rate (between 0 and 1).

### toString

    public java.lang.String toString()

  Calls toString() with a default title.

  Overrides:
    `toString` in class `Matrix`
  Returns:
    the confusion matrix as a string

### toString

    public java.lang.String toString(java.lang.String title)

  Outputs the performance statistics as a classification confusion matrix. For each class value, shows the distribution of predicted class values.

  Parameters:
    `title` - the title for the confusion matrix
  Returns:
    the confusion matrix as a String

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