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Question: Explain the concept of cross-entropy loss in the context of classification problems.
Answer: Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. It penalizes models that are confidently wrong and is a common choice for binary and multiclass classification problems.

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