Binary cross-entropy (BCE) is the default loss function for binary classification—but it breaks down badly on imbalanced datasets. The reason is subtle but important: BCE weighs mistakes from both classes equally, even when one class is extremely rare.  Imagine two predictions: a minority-class sample with true label 1 predicted at 0.3, and a majority-class sample […] The post Focal Loss vs Binary Cross-Entropy: A Practical Guide for Imbalanced Classification appeared first on MarkTechPost .