Learning Path
Question & Answer1
Understand Question2
Review Options3
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Explore TopicChoose the Best Answer
A
Model Complexity
B
Classification Performance
C
Overfitting Risk
D
Feature Importance
Understanding the Answer
Let's break down why this is correct
Answer
Mean Squared Error is a loss function that measures how close predicted values are to the true values in regression, and it is often used to gauge predictive accuracy. Cross‑entropy does the same job for classification, measuring how well predicted probabilities match the true class labels. Therefore the relationship is that Cross‑Entropy is to classification accuracy what MSE is to predictive accuracy. For example, if a model predicts probabilities [0. 8,0.
Detailed Explanation
Cross‑entropy measures how well a model predicts the right class. Other options are incorrect because Loss functions do not tell how many parameters a model has; A low loss does not automatically mean the model is overfitting.
Key Concepts
Loss Functions
Predictive Performance
Classification
Topic
Loss Functions
Difficulty
medium level question
Cognitive Level
understand
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