robFScore: Robust F-Beta Score

In RobustMetrics: Calculates Robust Performance Metrics for Imbalanced Classification Problems

Robust F-Beta Score

Description

Compute a robust version of the F-Beta Score.

Usage

robFScore(
  actual = NULL,
  predicted = NULL,
  TP = NULL,
  FN = NULL,
  FP = NULL,
  TN = NULL,
  beta = 1,
  d0 = 0.1
)

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 the harmonic mean
d0	Weight of the estimated true positive probability in the harmonic mean

Details

Calculate the robust F-Beta Score F_{\beta,d_0} with two parameters. Provide either:

• actual and predicted or

• TP, FN, FP and TN.

If d_0=0, the robust F-Beta Score coincides with the F-Beta Score.

Value

robust F-Beta Score.

References

Holzmann, H., Klar, B. (2026). 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)
robFScore(actual, predicted, beta=1, d0=0.1)
robFScore(TP=4, FN=2, FP=1, TN=3, beta=1, d0=1)

RobustMetrics documentation built on Aug. 21, 2026, 5:17 p.m.