In multi-class classification, which loss function is best suited for optimizing the separation between classes while allowing for margin-based errors?

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Hinge Loss

Logistic Loss

Neyman-Pearson Loss

Cross-Entropy Loss

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Answer

In multi‑class classification we often want a decision rule that keeps each class far from the others, so the loss must penalize predictions that lie too close to the decision boundary. The multiclass hinge loss, also called the structured‑SVM loss, does exactly that by adding a margin term that forces the correct class score to exceed all others by a fixed amount. If the margin is violated, the loss grows linearly, encouraging the model to push the correct class further apart. For example, if a sample belongs to class A but the model scores class B higher by 0. 3, the hinge loss will add a penalty of 0.

Detailed Explanation

This loss pushes the score of the correct class higher than the others by a fixed margin. Other options are incorrect because Many think logistic loss can create margins, but it only measures how well the model predicts probabilities; Neyman‑Pearson loss is about controlling one type of error, not about separating classes.

Key Concepts

Multi-class Loss Functions

Margin-based Classification

Support Vector Machines

Topic

Multi-class Loss Functions

Difficulty

hard level question

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understand

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