In data mining, a treatment learner is a program used to find rules that change the expected class distribution (compared to some baseline). A classifier is a treatment learned used for recognition tasks, such as identifying defective items in an assembly line.

Treatment learners include the J48, J48part, and APRIORI for discrete classes, TAR2 and TAR3 for weighted discrete classes, and the M5 for continuous classes.

Treatment learners are used for planning some minimal action to improve the odds that something will be later be recognized as belonging to some class. A treatment learner could be used to make repairs to the defective toys rejected from the assembly line.

Treatment learners are all about minimality - what is the least you need to do to most effect something.

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[1] T. Menzies. Introduction to Treatment Learning. Department of Computer Science, Portland State University.