public class FScore extends java.lang.Object implements ClassificationMetric
The traditional or balanced Fscore (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 Fscore measures the effectiveness of retrieval with respect to a user who attaches β times as much importance to recall as precision.
Modifier and Type  Field and Description 

static FScore 
F1
The F_1 score, the harmonic mean of precision and recall.

static FScore 
F2
The F_2 score, which weighs recall higher than precision.

static FScore 
FHalf
The F_0.5 score, which weighs recall lower than precision.

Constructor and Description 

FScore()
Constructor of F1 score.

FScore(double beta)
Constructor of general Fscore.

Modifier and Type  Method and Description 

static double 
of(double beta,
int[] truth,
int[] prediction)
Calculates the F1 score.

double 
score(int[] truth,
int[] prediction)
Returns a score to measure the quality of classification.

java.lang.String 
toString() 
public static final FScore F1
public static final FScore F2
public static final FScore FHalf
public FScore()
public FScore(double beta)
beta
 a positive value such that Fscore measures
the effectiveness of retrieval with respect
to a user who attaches β times as much
importance to recall as precision.public double score(int[] truth, int[] prediction)
ClassificationMetric
score
in interface ClassificationMetric
truth
 the true class labels.prediction
 the predicted class labels.public static double of(double beta, int[] truth, int[] prediction)
beta
 a positive value such that Fscore measures
the effectiveness of retrieval with respect
to a user who attaches β times as much
importance to recall as precision.public java.lang.String toString()
toString
in class java.lang.Object