Learning Path
Question & Answer
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Hinge loss
Logistic loss
Neyman-Pearson loss
Cross-entropy loss
Understanding the Answer
Let's break down why this is correct
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
hard level question
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Deep Dive: Multi-class Loss Functions
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Definition
Multi-class loss functions are designed to evaluate the performance of multi-class classification models by penalizing incorrect predictions. They include Neyman-Pearson loss, hinge loss, and logistic loss, each serving different optimization and evaluation purposes.
Topic Definition
Multi-class loss functions are designed to evaluate the performance of multi-class classification models by penalizing incorrect predictions. They include Neyman-Pearson loss, hinge loss, and logistic loss, each serving different optimization and evaluation purposes.
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