Decision rule

In decision theory, a decision rule is a function which maps an observation to an appropriate action. Decision rules play an important role in the theory of statistics and economics, and are closely related to the concept of a strategy in game theory.

In order to evaluate the usefulness of a decision rule, it is necessary to have a loss function detailing the outcome of each action under different states.

Formal definition
Given an observable random variable X over the probability space $$ \scriptstyle (\mathcal{X},\Sigma, P_\theta)$$, determined by a parameter &theta; &isin; &Theta;, and a set A of possible actions, a (deterministic) decision rule is a function &delta; : $$\scriptstyle\mathcal{X}$$&rarr; A.

Examples of decision rules

 * An estimator is a decision rule used for estimating a parameter. In this case the set of actions is the parameter space, and a loss function details the cost of the discrepancy between the true value of the parameter and the estimated value.
 * Out of sample prediction in regression and classification models.