RobustMetrics (version 0.1.1)

FScore: F-Beta Score

Description

Compute the F-Beta Score.

Usage

FScore(
  actual = NULL,
  predicted = NULL,
  TP = NULL,
  FN = NULL,
  FP = NULL,
  TN = NULL,
  beta = 1
)

Value

F-Beta Score.

Arguments

actual

A vector of actual values (1/0 or TRUE/FALSE)

predicted

A vector of prediction values (1/0 or TRUE/FALSE)

TP

Count of true positives (correctly predicted 1/TRUE)

FN

Count of false negatives (predicted 0/FALSE, but actually 1/TRUE)

FP

Count of false positives (predicted 1/TRUE, but actually 0/FALSE)

TN

Count of true negatives (correctly predicted 0/FALSE)

beta

Beta squared is the weight of recall in harmonic mean

Details

Calculate the F-Beta Score. Provide either:

• actual and predicted or

• TP, FN, FP and TN.

References

Holzmann, H., Klar, B. (2024). Robust performance metrics for imbalanced classification problems. arXiv:2404.07661. LINK

Examples

actual <-    c(1,1,1,1,1,1,0,0,0,0)
predicted <- c(1,1,1,1,0,0,1,0,0,0)
FScore(actual, predicted)
FScore(TP=4, FN=2, FP=1, TN=3)