A data scientist is developing a multi-class classification model to categorize images of animals into classes such as 'dog', 'cat', and 'bird'. They need to choose a loss function to ensure that the model not only predicts accurately but also minimizes the misclassification of similar-looking animals. Which loss function should the data scientist prioritize in this scenario?

For a multi‑class image classifier the most common choice is the cross‑entropy loss, because it directly measures the probability gap between the true class and the predicted distribution. To make the model especially careful about confusing similar animals, you can use a weighted or focal version of cross‑entropy that increases the penalty for hard or frequently confused examples. This way the loss grows more steeply when the model predicts a cat instead of a dog, for instance, and the network learns to distinguish subtle visual cues. In practice, you would give a higher weight to the dog‑cat pair in the loss function and train the network as usual. This approach keeps accuracy high while actively discouraging misclassifications between visually similar classes.