Catalano.MachineLearning

Class ConfusionMatrix

java.lang.Object

Catalano.MachineLearning.ConfusionMatrix

public class ConfusionMatrix
extends java.lang.Object

Confusion matrix. In the field of machine learning, a confusion matrix, also known as a contingency table or an error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning.

Constructor Summary

Constructors

Constructor and Description
ConfusionMatrix(int[][] confusionMatrix) Initializes a new instance of the ConfusionMatrix class.

Method Summary

Modifier and Type	Method and Description
static int[][]	Create(IClassifier classifier, double[][] validation, int[] labels) Create confusion matrix.
double[]	getFalsePositiveRate() Get False Positive rate.
double[]	getFMeasure() Get F-Measure.
double[]	getPrecision() Get Precision.
double[]	getTruePositiveRate() Get True Positive rate.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ConfusionMatrix

public ConfusionMatrix(int[][] confusionMatrix)

Initializes a new instance of the ConfusionMatrix class.

Parameters:

confusionMatrix - ConfusionMatrix.

Method Detail

Create

public static int[][] Create(IClassifier classifier,
                             double[][] validation,
                             int[] labels)

Create confusion matrix.

Parameters:

classifier - Classifier.

validation - Data validation (input).

labels - Labels (output).

Returns:

Confusion matrix.

getTruePositiveRate

public double[] getTruePositiveRate()

Get True Positive rate.

Returns:

TP rate.

getFalsePositiveRate

public double[] getFalsePositiveRate()

Get False Positive rate.

Returns:

FP rate.

getPrecision

public double[] getPrecision()

Get Precision.

Returns:

Precision.

getFMeasure

public double[] getFMeasure()

Get F-Measure.

Returns:

F-Measure.