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Question & Answer
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penalizes incorrect class assignments
minimizes the distance to the decision boundary
maximizes true positives
focuses on false negatives
Understanding the Answer
Let's break down why this is correct
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
medium 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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