In a multi-class classification scenario, which loss function is best suited for maximizing the margin between classes while allowing some misclassifications?

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Hinge loss

Logistic loss

Neyman-Pearson loss

Cross-entropy loss

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Answer

The multiclass hinge loss, used in support vector machines, is ideal for this because it explicitly maximizes a margin between the correct class and the others while still letting some points fall inside the margin as slack. The loss adds a penalty when the score of the true class is not larger than the best competing class by a set margin, and it tolerates violations by assigning a linear penalty. This approach keeps the decision boundary sharp but still allows misclassifications when the data are noisy or overlapping. For example, if a sample belongs to class A but its score is only 0. 2 higher than class B, the hinge loss will increase proportionally to that difference, encouraging the model to separate the classes more while not forcing perfect separation.

Detailed Explanation

Hinge loss pushes the decision boundary away from data points, creating a clear gap or margin between classes. Other options are incorrect because Many think logistic loss works for margins because it uses probabilities, but it mainly focuses on how likely each class is, not on keeping classes apart; Neyman-Pearson loss is about controlling false positives and negatives, not about keeping classes apart.

Key Concepts

Multi-class Loss Functions

Classification Margin

Model Evaluation

Topic

Multi-class Loss Functions

Difficulty

hard level question

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understand

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