smile.validation

Class FMeasure

java.lang.Object

smile.validation.FMeasure

All Implemented Interfaces:

ClassificationMeasure

public class FMeasure
extends java.lang.Object
implements ClassificationMeasure

The F-score (or F-measure) considers both the precision and the recall of the test to compute the score. The precision p is the number of correct positive results divided by the number of all positive results, and the recall r is the number of correct positive results divided by the number of positive results that should have been returned. The traditional or balanced F-score (F1 score) is the harmonic mean of precision and recall, where an F1 score reaches its best value at 1 and worst at 0. The general formula involves a positive real β so that F-score measures the effectiveness of retrieval with respect to a user who attaches β times as much importance to recall as precision.

Field Summary

Fields

Modifier and Type	Field and Description
static FMeasure	instance

Constructor Summary

Constructors

Constructor and Description
FMeasure() Constructor of F1 score.
FMeasure(double beta) Constructor of general F-score.

Method Summary

Modifier and Type	Method and Description
double	measure(int[] truth, int[] prediction) Returns an index to measure the quality of classification.
static double	of(int[] truth, int[] prediction) Calculates the F1 score.

Methods inherited from class java.lang.Object

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

Field Detail

instance

public static final FMeasure instance

Constructor Detail

FMeasure

public FMeasure()

Constructor of F1 score.

FMeasure

public FMeasure(double beta)

Constructor of general F-score.

Parameters:

beta - a positive value such that F-score measures the effectiveness of retrieval with respect to a user who attaches β times as much importance to recall as precision.

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.

of

public static double of(int[] truth,
                        int[] prediction)

Calculates the F1 score.