The choice of loss function in multi-class classification has no significant impact on the overall model performance, as long as the evaluation metrics are correctly applied.

The choice of loss function does matter in multi‑class classification because it shapes how the model learns to separate classes during training. A loss that heavily penalizes misclassifications (like cross‑entropy) pushes the network to produce sharper probability distributions, while a loss that focuses on ranking (like hinge loss) may favor different decision boundaries. If the loss and the evaluation metric (e. g. , accuracy or F1) are mismatched, the model may optimize for the wrong objective and underperform on the metric you care about.