When selecting a loss function for a multi-class classification task, which factor is most crucial for ensuring model performance?

The most crucial factor is how well the loss function matches the evaluation metric and the problem’s characteristics. For a multi‑class classifier, a loss that directly optimizes the metric you care about—typically cross‑entropy when you evaluate with accuracy or log‑loss—helps the model learn the right probabilities. If the loss is misaligned, the model can converge to a solution that looks good on training data but performs poorly on the real metric. For example, training a softmax classifier with mean‑squared error will still fit the data but will not produce calibrated probabilities, hurting accuracy. Therefore, choosing a loss that reflects the desired metric and the data distribution is key to good performance.