Cross-entropy is a measure from the field of information theory that quantifies the difference between two probability distributions, typically the true distribution and the predicted distribution. This concept is crucial when evaluating how well a predicted probability distribution aligns with the actual outcomes, helping to assess model performance in classification tasks and beyond. Understanding cross-entropy allows for better insights into entropy, joint entropy, and conditional entropy, as it builds upon these foundational ideas.
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