Focal Loss vs Binary Cross-Entropy: A Practical Guide for Imbalanced Classification

Focal Loss addresses critical limitations of Binary Cross-Entropy in imbalanced classification. By dynamically adjusting loss weights, it helps neural networks handle datasets with rare minority classes more effectively, improving model performance and accuracy.