Log loss, also known as logistic loss or cross-entropy loss, is a performance metric used to evaluate the accuracy of a classification model. It quantifies the difference between the predicted probabilities and the actual class labels, with a focus on penalizing incorrect predictions. The lower the log loss value, the better the model's predictions are aligned with the true outcomes. It is particularly useful in binary classification tasks, but can be extended to multiclass problems as well. Common applications include evaluating models in machine learning competitions and assessing the effectiveness of classifiers in various domains.
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