Focal Loss vs Binary Cross-Entropy: A Practical Guide for Imbalanced Classification
Binary cross-entropy (BCE) is the default loss operate for binary classification—however it breaks down badly on imbalanced datasets. The cause is delicate however necessary: BCE weighs errors from each courses equally, even when one class is extraordinarily uncommon. Imagine two predictions: a minority-class pattern with true label 1 predicted at 0.3, and a majority-class pattern…
