Loss functions for classification

Concept in machine learning

In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems. Given as the space of all possible inputs, and as the set of labels, a typical goal of classification algorithms is to find a function which best predicts a label for a given input . However, because of incomplete information, noise in the measurement, or probabilistic components in the underlying process, it is possible for the same to generate different . As a result, the goal of the learning problem is to minimize expected loss, defined as

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