Which of the following loss functions are suitable for evaluating the performance of multi-class classification models? Select all that apply.

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Neyman-Pearson loss

Mean Squared Error

Hinge loss

Logistic loss

Cross-Entropy loss

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Answer

For multi‑class classification you normally use a loss that measures how close the predicted class probabilities are to the true labels. The most common choice is categorical cross‑entropy (also called softmax cross‑entropy) because it works with the softmax output and penalises incorrect probabilities strongly. Another option is the multiclass hinge loss used by linear SVMs, which encourages a margin between the correct class and the others. Finally, the Kullback‑Leibler divergence can be used when the output is a probability distribution rather than a single class. Each of these losses is designed to handle more than two classes and to train a model that outputs a probability for each class.

Detailed Explanation

This loss compares the predicted probability distribution to the true class. Other options are incorrect because This loss is designed for hypothesis testing, not for training classifiers; It measures the squared difference between numbers, which is useful for predicting continuous values.

Key Concepts

Multi-class classification

Loss functions

Optimization methods

Topic

Multi-class Loss Functions

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medium level question

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understand

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