smile.validation

Class MCCMeasure

java.lang.Object

smile.validation.MCCMeasure

All Implemented Interfaces:

ClassificationMeasure

public class MCCMeasure
extends java.lang.Object
implements ClassificationMeasure

MCC is a correlation coefficient between prediction and actual values. It is considered as a balanced measure for binary classification, even in unbalanced data sets. It varies between -1 and +1. 1 when there is perfect agreement between ground truth and prediction, -1 when there is a perfect disagreement between ground truth and predictions. MCC of 0 means the model is not better then random.

See Also:

Matthews correlation coefficient

Constructor Summary

Constructors

Constructor and Description
MCCMeasure()

Method Summary

Modifier and Type	Method and Description
double	measure(int[] truth, int[] prediction) Returns an index to measure the quality of classification.

Methods inherited from class java.lang.Object

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

Constructor Detail

MCCMeasure

public MCCMeasure()

Method Detail

measure

public double measure(int[] truth,
                      int[] prediction)

Description copied from interface: ClassificationMeasure

Returns an index to measure the quality of classification.

Specified by:

measure in interface ClassificationMeasure

Parameters:

truth - the true class labels.

prediction - the predicted class labels.