Learning Path
Question & Answer
Choose the Best Answer
Hinge Loss → B. Logistic Loss → C. Neyman-Pearson Loss → D. Cross-Entropy Loss
Neyman-Pearson Loss → A. Hinge Loss → D. Cross-Entropy Loss → C. Logistic Loss
Logistic Loss → D. Cross-Entropy Loss → A. Hinge Loss → B. Neyman-Pearson Loss
Cross-Entropy Loss → C. Logistic Loss → B. Neyman-Pearson Loss → A. Hinge Loss
Understanding the Answer
Let's break down why this is correct
The sequence starts with hinge loss, then logistic loss, then Neyman-Pearson loss, and ends with cross‑entropy loss. Other options are incorrect because The idea that Neyman‑Pearson loss is the best for multi‑class comes from its use in controlling false alarms, but it does not train a probability model; Thinking logistic loss is the top choice ignores that cross‑entropy gives a sharper gradient for probability estimation.
Key Concepts
Multi-class Loss Functions
easy level question
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Deep Dive: Multi-class Loss Functions
Master the fundamentals
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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