log loss
Log loss, also known as logarithmic loss or cross-entropy loss, is a commonly used performance metric in evaluating the accuracy of a probabilistic classification model. It measures the difference between the predicted probabilities assigned by the model and the actual true classes, penalizing inaccurate predictions with higher loss values. Lower log loss values indicate better model performance, with zero being the best possible score.
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