RobustMetrics (version 0.1.1)

robFScore2: General robust F-Beta Score

Description

Compute a robust version of the F-Beta Score with two additional parameters.

Usage

robFScore2(
  actual = NULL,
  predicted = NULL,
  TP = NULL,
  FN = NULL,
  FP = NULL,
  TN = NULL,
  d1 = 1,
  d0 = 0.1,
  c = 1
)

Value

robust F-Beta Score with two additional parameters.

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)

d1

Weight of recall in the harmonic mean (corresponds to beta squared)

d0

Weight of the estimated true positive probability in the harmonic mean

c

Additional parameter in numerator

Details

Calculate the robust F-Beta Score \(F_{rb}\) with two additional parameters. Provide either:

• actual and predicted or

• TP, FN, FP and TN.

If \(d_1=\beta^2, d_0=c=0\), the robust F-Beta Score coincides with the F-Beta Score.

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)
robFScore2(actual, predicted, d0 = 0.1, c = 0.1)
robFScore2(TP=4, FN=2, FP=1, TN=3, d0 = 0.1, c = 1)