Probability matching

Probability matching

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 Harvtxt|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.

References

*Harvard reference | Surname1=Duda| Given1=Richard O.| Surname2=Hart| Given2=Peter E. | Surname3=Stork| Given3=David G. |Title=Pattern Classification| Publisher=John Wiley & Sons| Place=New York | Year=2001| Edition=2| URL=http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471056693.html


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