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Logistic Regression
Logistic Regression
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In binary classification, what happens to the cross-entropy loss when the model predicts probability 0.01 for the true class?
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A.
The loss is exactly 1.0 because the model made a wrong prediction, and cross-entropy assigns unit loss to each misclassification
B.
The loss is 0.01 because cross-entropy directly equals the predicted probability for the incorrect class in binary classification
C.
The loss is -log(0.01) which equals approximately 4.6, a very high penalty since the model is confidently wrong about this example
D.
The loss is 0.99 because binary cross-entropy measures the complement of the predicted probability for the correct class
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