Given a multi-class classification scenario with the following loss functions: 1) Cross-Entropy Loss, 2) Hinge Loss, 3) Mean Squared Error, and 4) Focal Loss, which loss function would be most appropriate for a model designed to classify images of handwritten digits with a focus on reducing the impact of misclassifying less frequent digits?

The best choice is focal loss, because it adds a factor that down‑weights easy, common examples and up‑weights hard, rare ones, so the model pays more attention to misclassifying the less frequent digits. Cross‑entropy treats all classes equally, so the model may ignore the minority digits. Hinge loss is designed for binary margins and does not handle class imbalance well. Mean squared error is not ideal for categorical outputs. For instance, if the digit “8” appears rarely, focal loss will increase its gradient, encouraging the network to learn a better decision boundary for that class.