Baseline negative log likelihood.
Loss weight across confidence
FL = −0.25(1 − pₜ)² log(pₜ)
α(1 − pₜ)^γ multiplier.
Weighted contribution from this example.
Why dense imbalance can dominate training.
Focal loss changes allocation, not evidence.
Gamma suppresses easy cases
At γ = 0, focal loss reduces to α-balanced cross-entropy. Larger γ increasingly down-weights high-pₜ examples.
Alpha handles class asymmetry
α can rebalance positives and negatives, but it should be tuned against prevalence and the chosen decision metric.
Evaluation remains task-specific
Track precision-recall, calibration, rare cohorts, false negatives, and threshold behavior. Lower training loss is not the deployment objective.