Probability matching

From Wikipedia, the free encyclopedia

Probability matching is a suboptimal decision strategy in which predictions of class membership are proportional to the class base rates. Thus, if in the training set positive examples are observed 60% of the time, and negative examples are observed 40% of the time, the observer using a probability-matching strategy will predict (for unlabeled examples) a class label of "positive" on 60% of instances, and a class label of "negative" on 40% of instances.

The optimal Bayesian decision strategy (to maximize the number of correct predictions, see Duda, Hart & Stork (2001)) in such a case is to always predict "positive" (i.e., predict the majority category in the absence of other information). The suboptimal probability-matching strategy is of psychological interest because it is frequently employed by human subjects in decision and classification studies.

[edit] References

• Duda, Richard O.; Peter E. Hart & David G. Stork (2001), written at New York, Pattern Classification (2 ed.), John Wiley & Sons, <http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471056693.html>