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In
mathematical optimization Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfi ...
and
decision theory Decision theory (or the theory of choice; not to be confused with choice theory) is a branch of applied probability theory concerned with the theory of making decisions based on assigning probabilities to various factors and assigning numerical ...
, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a
real number In mathematics, a real number is a number that can be used to measurement, measure a ''continuous'' one-dimensional quantity such as a distance, time, duration or temperature. Here, ''continuous'' means that values can have arbitrarily small var ...
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 opposite (in specific domains, variously called a reward function, a profit function, a
utility function As a topic of economics, utility is used to model worth or value. Its usage has evolved significantly over time. The term was introduced initially as a measure of pleasure or happiness as part of the theory of utilitarianism by moral philosoph ...
, a fitness function, etc.), in which case it is to be maximized. The loss function could include terms from several levels of the hierarchy. 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. The concept, as old as Laplace, was reintroduced in statistics by
Abraham Wald Abraham Wald (; hu, Wald Ábrahám, yi, אברהם וואַלד;  – ) was a Jewish Hungarian mathematician who contributed to decision theory, geometry, and econometrics and founded the field of statistical sequential analysis. One o ...
in the middle of the 20th century. In the context of
economics Economics () is the social science that studies the production, distribution, and consumption of goods and services. Economics focuses on the behaviour and interactions of economic agents and how economies work. Microeconomics analy ...
, 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 premiums, particularly since the works of Harald Cramér in the 1920s. In optimal control, the loss is the penalty for failing to achieve a desired value. In
financial risk management Financial risk management is the practice of protecting Value (economics), economic value in a business, firm by using financial instruments to manage exposure to financial risk - principally operational risk, credit risk and market risk, with more ...
, the function is mapped to a monetary loss.


Examples


Regret

Leonard J. Savage argued that using non-Bayesian methods such as minimax, the loss function should be based on the idea of '' regret'', i.e., the loss associated with a decision should be the difference between the consequences of the best decision that could have been made had the underlying circumstances been known and the decision that was in fact taken before they were known.


Quadratic loss function

The use of a
quadratic In mathematics, the term quadratic describes something that pertains to squares, to the operation of squaring, to terms of the second degree, or equations or formulas that involve such terms. ''Quadratus'' is Latin for ''square''. Mathematics ...
loss function is common, for example when using
least squares The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the r ...
techniques. It is often more mathematically tractable than other loss functions because of the properties of
variance In probability theory and statistics, variance is the expectation of the squared deviation of a random variable from its population mean or sample mean. Variance is a measure of dispersion, meaning it is a measure of how far a set of number ...
s, as well as being symmetric: an error above the target causes the same loss as the same magnitude of error below the target. If the target is ''t'', then a quadratic loss function is :\lambda(x) = C (t-x)^2 \; for some constant ''C''; the value of the constant makes no difference to a decision, and can be ignored by setting it equal to 1. This is also known as the squared error loss (SEL). Many common
statistic A statistic (singular) or sample statistic is any quantity computed from values in a sample which is considered for a statistical purpose. Statistical purposes include estimating a population parameter, describing a sample, or evaluating a hy ...
s, including t-tests,
regression Regression or regressions may refer to: Science * Marine regression, coastal advance due to falling sea level, the opposite of marine transgression * Regression (medicine), a characteristic of diseases to express lighter symptoms or less extent ( ...
models,
design of experiments The design of experiments (DOE, DOX, or experimental design) is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associ ...
, and much else, use
least squares The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the r ...
methods applied using
linear regression In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is ...
theory, which is based on the quadratic loss function. The quadratic loss function is also used in linear-quadratic optimal control problems. In these problems, even in the absence of uncertainty, it may not be possible to achieve the desired values of all target variables. Often loss is expressed as a quadratic form in the deviations of the variables of interest from their desired values; this approach is tractable because it results in linear first-order conditions. In the context of stochastic control, the expected value of the quadratic form is used.


0-1 loss function

In statistics and
decision theory Decision theory (or the theory of choice; not to be confused with choice theory) is a branch of applied probability theory concerned with the theory of making decisions based on assigning probabilities to various factors and assigning numerical ...
, a frequently used loss function is the ''0-1 loss function'' : L(\hat, y) = I(\hat \ne y), \, where I is the
indicator function In mathematics, an indicator function or a characteristic function of a subset of a set is a function that maps elements of the subset to one, and all other elements to zero. That is, if is a subset of some set , one has \mathbf_(x)=1 if x ...
.


Constructing loss and objective functions

In many applications, objective functions, including loss functions as a particular case, are determined by the problem formulation. In other situations, the decision maker’s preference must be elicited and represented by a scalar-valued function (called also utility function) in a form suitable for optimization — the problem that Ragnar Frisch has highlighted in his Nobel Prize lecture. The existing methods for constructing objective functions are collected in the proceedings of two dedicated conferences. In particular, Andranik Tangian showed that the most usable objective functions — quadratic and additive — are determined by a few indifference points. He used this property in the models for constructing these objective functions from either ordinal or cardinal data that were elicited through computer-assisted interviews with decision makers. Among other things, he constructed objective functions to optimally distribute budgets for 16 Westfalian universities and the European subsidies for equalizing unemployment rates among 271 German regions.


Expected loss

In some contexts, the value of the loss function itself is a random quantity because it depends on the outcome of a random variable ''X''.


Statistics

Both frequentist and Bayesian statistical theory involve making a decision based on the
expected value In probability theory, the expected value (also called expectation, expectancy, mathematical expectation, mean, average, or first moment) is a generalization of the weighted average. Informally, the expected value is the arithmetic mean of a ...
of the loss function; however, this quantity is defined differently under the two paradigms.


Frequentist expected loss

We first define the expected loss in the frequentist context. It is obtained by taking the expected value with respect to the probability distribution, ''P''''θ'', of the observed data, ''X''. This is also referred to as the risk function of the decision rule ''δ'' and the parameter ''θ''. Here the decision rule depends on the outcome of ''X''. The risk function is given by: : R(\theta, \delta) = \operatorname_\theta L\big( \theta, \delta(X) \big) = \int_X L\big( \theta, \delta(x) \big) \, \mathrm P_\theta (x) . Here, ''θ'' is a fixed but possibly unknown state of nature, ''X'' is a vector of observations stochastically drawn from a
population Population typically refers to the number of people in a single area, whether it be a city or town, region, country, continent, or the world. Governments typically quantify the size of the resident population within their jurisdiction using ...
, \operatorname_\theta is the expectation over all population values of ''X'', ''dP''''θ'' is a
probability measure In mathematics, a probability measure is a real-valued function defined on a set of events in a probability space that satisfies measure properties such as ''countable additivity''. The difference between a probability measure and the more g ...
over the event space of ''X'' (parametrized by ''θ'') and the integral is evaluated over the entire support of ''X''.


Bayesian expected loss

In a Bayesian approach, the expectation is calculated using the posterior distribution * of the parameter ''θ'': :\rho(\pi^*,a) = \int_\Theta L(\theta, a) \, \mathrm \pi^* (\theta). One then should choose the action ''a*'' which minimises the expected loss. Although this will result in choosing the same action as would be chosen using the frequentist risk, the emphasis of the Bayesian approach is that one is only interested in choosing the optimal action under the actual observed data, whereas choosing the actual frequentist optimal decision rule, which is a function of all possible observations, is a much more difficult problem.


Examples in statistics

* For a scalar parameter ''θ'', a decision function whose output \hat\theta is an estimate of ''θ'', and a quadratic loss function (
squared error loss In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cos ...
) L(\theta,\hat\theta)=(\theta-\hat\theta)^2, the risk function becomes the
mean squared error In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference betwe ...
of the estimate, R(\theta,\hat\theta)= \operatorname_\theta(\theta-\hat\theta)^2.An Estimator found by minimizing the
Mean squared error In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference betwe ...
estimates the Posterior distribution's mean. * In density estimation, the unknown parameter is probability density itself. The loss function is typically chosen to be a norm in an appropriate function space. For example, for ''L''2 norm, L(f,\hat f) = \, f-\hat f\, _2^2\,, the risk function becomes the mean integrated squared error R(f,\hat f)=\operatorname \, f-\hat f\, ^2.\,


Economic choice under uncertainty

In economics, decision-making under uncertainty is often modelled using the von Neumann–Morgenstern utility function of the uncertain variable of interest, such as end-of-period wealth. Since the value of this variable is uncertain, so is the value of the utility function; it is the expected value of utility that is maximized.


Decision rules

A decision rule makes a choice using an optimality criterion. Some commonly used criteria are: * Minimax: Choose the decision rule with the lowest worst loss — that is, minimize the worst-case (maximum possible) loss: \underset \ \max_ \ R(\theta,\delta). *
Invariance Invariant and invariance may refer to: Computer science * Invariant (computer science), an expression whose value doesn't change during program execution ** Loop invariant, a property of a program loop that is true before (and after) each iterat ...
: Choose the decision rule which satisfies an invariance requirement. *Choose the decision rule with the lowest average loss (i.e. minimize the
expected value In probability theory, the expected value (also called expectation, expectancy, mathematical expectation, mean, average, or first moment) is a generalization of the weighted average. Informally, the expected value is the arithmetic mean of a ...
of the loss function): \underset \operatorname_ (\theta,\delta)= \underset \ \int_ R(\theta,\delta) \, p(\theta) \,d\theta.


Selecting a loss function

Sound statistical practice requires selecting an estimator consistent with the actual acceptable variation experienced in the context of a particular applied problem. Thus, in the applied use of loss functions, selecting which statistical method to use to model an applied problem depends on knowing the losses that will be experienced from being wrong under the problem's particular circumstances. A common example involves estimating " location". Under typical statistical assumptions, the
mean There are several kinds of mean in mathematics, especially in statistics. Each mean serves to summarize a given group of data, often to better understand the overall value ( magnitude and sign) of a given data set. For a data set, the '' ari ...
or average is the statistic for estimating location that minimizes the expected loss experienced under the squared-error loss function, while the median is the estimator that minimizes expected loss experienced under the absolute-difference loss function. Still different estimators would be optimal under other, less common circumstances. In economics, when an agent is risk neutral, the objective function is simply expressed as the expected value of a monetary quantity, such as profit, income, or end-of-period wealth. For risk-averse or risk-loving agents, loss is measured as the negative of a
utility function As a topic of economics, utility is used to model worth or value. Its usage has evolved significantly over time. The term was introduced initially as a measure of pleasure or happiness as part of the theory of utilitarianism by moral philosoph ...
, and the objective function to be optimized is the expected value of utility. Other measures of cost are possible, for example mortality or morbidity in the field of
public health Public health is "the science and art of preventing disease, prolonging life and promoting health through the organized efforts and informed choices of society, organizations, public and private, communities and individuals". Analyzing the det ...
or safety engineering. For most optimization algorithms, it is desirable to have a loss function that is globally continuous and differentiable. Two very commonly used loss functions are the squared loss, L(a) = a^2, and the absolute loss, L(a)=, a, . However the absolute loss has the disadvantage that it is not differentiable at a=0. The squared loss has the disadvantage that it has the tendency to be dominated by outliers—when summing over a set of a's (as in \sum_^n L(a_i) ), the final sum tends to be the result of a few particularly large ''a''-values, rather than an expression of the average ''a''-value. The choice of a loss function is not arbitrary. It is very restrictive and sometimes the loss function may be characterized by its desirable properties. Among the choice principles are, for example, the requirement of completeness of the class of symmetric statistics in the case of i.i.d. observations, the principle of complete information, and some others. W. Edwards Deming and Nassim Nicholas Taleb argue that empirical reality, not nice mathematical properties, should be the sole basis for selecting loss functions, and real losses often are not mathematically nice and are not differentiable, continuous, symmetric, etc. For example, a person who arrives before a plane gate closure can still make the plane, but a person who arrives after can not, a discontinuity and asymmetry which makes arriving slightly late much more costly than arriving slightly early. In drug dosing, the cost of too little drug may be lack of efficacy, while the cost of too much may be tolerable toxicity, another example of asymmetry. Traffic, pipes, beams, ecologies, climates, etc. may tolerate increased load or stress with little noticeable change up to a point, then become backed up or break catastrophically. These situations, Deming and Taleb argue, are common in real-life problems, perhaps more common than classical smooth, continuous, symmetric, differentials cases.


See also

* Bayesian regret * Loss functions for classification *
Discounted maximum loss Discounted maximum loss, also known as worst-case risk measure, is the present value of the worst-case scenario for a financial portfolio. In investment, in order to protect the value of an investment, one must consider all possible alternatives ...
* Hinge loss * Scoring rule * Statistical risk


References


Further reading

* * * * * {{DEFAULTSORT:Loss Function Optimal decisions *