Log loss is a classification loss function that penalizes incorrect probability estimates, especially confident incorrect predictions. It is commonly used in logistic regression and probabilistic classifiers, where lower values indicate better alignment between predicted probabilities and true labels.
In classification , an evaluation metric that characterizes the accuracy of a classifier. The smaller log loss is, the more accurate a classifier is.
The loss function used in binary logistic regression. The following formula calculates Log Loss: where: - \\\\((x,y)\\\D\\\\) is the dataset containing many labeled examples, which are \\\\((x,y)\\\\) pairs. - \\\\(y\\\\) is the label in a labeled example. Since this is logistic regression, every value of \\\\(y\\\\) must either be 0 or 1. - \\\\(y'\\\\) is the predicted value (somewhere between 0 and 1, exclusive), given the set of features in \\\\(x\\\\). See Logistic regression: Loss and regularization in Machine Learning Crash Course for more information.