Loss Function - Page 2

A loss function converts the difference between a model’s output and the desired result into a number that training can minimize. Different tasks require different definitions of error: classification may penalize incorrect class probabilities, regression may measure numerical distance, and generative models may combine several objectives. The chosen loss shapes what the system learns, including which mistakes matter most and how imbalanced examples are treated. A falling training loss does not prove that the model will work well in production, because it can memorize data or optimize a proxy that misses the real goal. Validation metrics, subgroup analysis, and domain-specific costs must therefore accompany the training objective.