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Question & Answer
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Hinge Loss
Logistic Loss
Neyman-Pearson Loss
Cross-Entropy Loss
Understanding the Answer
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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
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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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