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 […]
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