Log Loss

Log loss evaluates not only whether a classification model predicts the correct label, but also how confidently it does so. If the model assigns high confidence to the wrong class, the loss increases sharply. This makes log loss especially meaningful for models that produce probability estimates. It is widely used in binary classification, logistic regression, and risk scoring systems. It encourages the production of well-calibrated probabilities rather than confidently wrong predictions.