A loss function tells a neural network how wrong its prediction is. Choosing the correct loss is essential because the model optimizes exactly what the loss measures, not necessarily the broader outcome a project owner has in mind.
Binary classification
Binary cross-entropy is the standard choice when each example belongs to one of two classes. In practice, frameworks often provide a version that accepts raw logits and applies the sigmoid operation internally. This combined implementation is usually more numerically stable than calculating probabilities first.
Single-label multiclass classification
Cross-entropy is appropriate when an example belongs to exactly one class among several options. The network produces one logit per class, and the loss rewards a high value for the correct class relative to the others. A softmax operation is commonly included inside the loss implementation.
Multilabel classification
When several labels can be true at the same time, each class should be treated as an independent binary decision. Binary cross-entropy across all labels is generally more suitable than multiclass cross-entropy. This distinction matters in medical imaging, where one scan may contain multiple findings.
Class imbalance changes the problem
A model can achieve high accuracy by ignoring a rare class. Class weights, focal loss, or balanced sampling can give difficult or uncommon examples more influence. These methods should be evaluated carefully because aggressive weighting may improve recall while reducing precision.
Connect loss to evaluation
The training loss and final business metric are related but not identical. Always evaluate precision, recall, F1 score, AUROC, calibration, and subgroup performance when they matter. A good loss creates a useful learning signal; a complete evaluation confirms whether the trained model behaves appropriately in the real task.