Logistic regression
Logistic regression typically optimizes the log loss for all the observations on which it is trained, which is the same as optimizing the average cross-entropy in the sample. For example, suppose we have formula_51 samples with each sample indexed by formula_99. The "average" of the loss function is then given by: