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Concept
Loss functions for classification
Summary
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 (problems of identifying which category a particular observation belongs to). Given as the space of all possible inputs (usually ), and as the set of labels (possible outputs), 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 (also known as the risk), defined as
where is a given loss function, and is the probability density function of the process that generated the data, which can equivalently be written as
Within classification, several commonly used loss functions are written solely in terms of the product of the true label and the predicted label . Therefore, they can be defined as functions of only one variable , so that with a suitably chosen function . These are called margin-based loss functions. Choosing a margin-based loss function amounts to choosing . Selection of a loss function within this framework impacts the optimal which minimizes the expected risk.
In the case of binary classification, it is possible to simplify the calculation of expected risk from the integral specified above. Specifically,
The second equality follows from the properties described above. The third equality follows from the fact that 1 and −1 are the only possible values for , and the fourth because . The term within brackets is known as the conditional risk.
One can solve for the minimizer of by taking the functional derivative of the last equality with respect to and setting the derivative equal to 0. This will result in the following equation
which is also equivalent to setting the derivative of the conditional risk equal to zero.
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