weka.classifiers.evaluation

Class ConfusionMatrix

java.lang.Object

weka.core.Matrix

weka.classifiers.evaluation.ConfusionMatrix

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, RevisionHandler

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: 1.9 $

Author:

Len Trigg (len@reeltwo.com)

See Also:

Serialized Form

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and Description
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.
java.lang.String	getRevision() Returns the revision string.
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

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class Matrix

Returns:

the revision