binary cross-entropy

Binary cross-entropy is a mathematical formula used to calculate the difference between predicted and actual outcomes in a binary classification problem, measuring the model's performance. It quantifies the level of dissimilarity between the predicted probability distribution and the observed distribution in a compact and understandable way.

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