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Recursive partitioning

Recursive partitioning is a statistical method for multivariable analysis. Recursive partitioning creates a decision tree that strives to correctly classify members of the population by splitting it into sub-populations based on several dichotomous independent variables. The process is termed recursive because each sub-population may in turn be split an indefinite number of times until the splitting process terminates after a particular stopping criterion is reached.

Advantages and disadvantages
Compared to other multivariable methods, recursive partitioning has advantages and disadvantages. • Advantages are: • Generates clinically more intuitive models that do not require the user to perform calculations. • Allows varying prioritizing of misclassifications in order to create a decision rule that has more sensitivity or specificity. • May be more accurate. • Disadvantages are: • Does not work well for continuous variables • May overfit data. ==Examples==
Examples
Examples are available of using recursive partitioning in research of diagnostic tests. Goldman used recursive partitioning to prioritize sensitivity in the diagnosis of myocardial infarction among patients with chest pain in the emergency room. == See also ==
Source: Wikipedia ↗
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