Loss Function in mathematical optimization, statistics, decision theory and machine learning is a function that maps an event or values of one or more variables onto a real number intuitively representing some “cost” associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its negative (sometimes called a reward function, a profit function, a utility function, a fitness function, etc.), in which case it is to be maximized. In statistics, typically a loss function is used for parameter estimation, and the event in question is some function of the difference between estimated and true values for an instance of data. In the context of economics, for example, this is usually economic cost or regret. In classification, it is the penalty for an incorrect classification of an example. In actuarial science, it is used in an insurance context to model benefits paid over. In optimal control, the loss is the penalty for failing to achieve the desired value. In financial risk management, the function is precisely mapped to a monetary loss.
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