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Multi-class Loss Functions
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Neyman-Pearson loss : penalizes false positives :: Logistic loss : ?

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A

penalizes incorrect class assignments

B

minimizes the distance to the decision boundary

C

maximizes true positives

D

focuses on false negatives

Understanding the Answer

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Logistic loss, also called log loss, measures how far the predicted probability is from the true label. Other options are incorrect because People think logistic loss pushes predictions toward the decision boundary; Some believe logistic loss rewards true positives.

Key Concepts

Multi-class Loss Functions
Classification Performance Evaluation
Optimization Techniques
Topic

Multi-class Loss Functions

Difficulty

medium level question

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understand

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Definition
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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