CMP.7:1 - Problem frame

Use this when examples or feedback are available and the required rule for further cases is missing, or an existing learning procedure must be changed for a different target, kind of feedback or resource limit. You need to construct the procedure that obtains the rule, rather than leave “learn the model” as an unexplained operation.

The situation occurs when learning a classifier, estimating a response, constructing a surrogate, discovering a program or updating a policy. The output may be a symbolic rule, a function represented by weights, a distribution or a retained set of candidate rules. Its required form follows from the intended use.

The gain is a learning procedure with explicit inputs, selection or update operations and a learned result that can be applied at its warranted scope. The reader needs functions, finite examples and a loss or another stated comparison. Probabilistic guarantees require the corresponding additional probability model; no one statistical sampling model is imposed on every learning problem.

Use an existing rule directly when it already serves the purpose. Use an established learner when its inputs, target, restrictions and costs fit. This pattern is needed when those choices or their connection to further use must be constructed or revised. Explaining a learned rule and teaching a person to apply it are distinct tasks, supplied by the explanation and development methods when required.