▁mult ivari able ▁analysis . ▁Rec ursive ▁partition ing ▁creates ▁a ▁decision ▁tree ▁that ▁st riv es ▁to ▁correctly ▁class ify ▁members ▁of ▁the ▁population ▁by ▁splitting ▁it ▁into ▁sub - pop ulations ▁based ▁on ▁several ▁dich ot om ous ▁independent ▁variables . ▁ ▁The ▁process ▁is ▁term ed ▁recursive ▁because ▁each ▁sub - pop ulation ▁may ▁in ▁turn ▁be ▁split ▁an ▁in defin ite ▁number ▁of ▁times ▁until ▁the ▁splitting ▁process ▁termin ates ▁after ▁a ▁particular ▁stopping ▁criter ion ▁is ▁reached . ▁ ▁Rec ursive ▁partition ing ▁methods ▁have ▁been ▁developed ▁since ▁the ▁ 1 9 8 0 s . ▁Well ▁known ▁methods ▁of ▁recursive ▁partition ing ▁include ▁Ross ▁Qu in lan ' s ▁ID 3 ▁algorithm ▁and ▁its ▁success ors , ▁C 4 . 5 ▁and ▁C 5 . 0 ▁and ▁Classification ▁and ▁Reg ression ▁Tre es . ▁Ens emble ▁learning ▁methods ▁such ▁as ▁Random ▁For ests ▁help ▁to ▁overcome ▁a ▁common ▁criticism ▁of ▁these ▁methods ▁- ▁their ▁vulner ability ▁to ▁ ▁over f itting ▁of ▁the ▁data ▁- ▁by ▁employ ing ▁different ▁algorithms ▁and ▁combining ▁their ▁output ▁in ▁some ▁way . ▁ ▁This ▁article ▁focus es ▁on ▁recursive ▁partition ing ▁for ▁medical ▁di agnostic ▁tests , ▁but ▁the ▁technique ▁has ▁far ▁wider ▁applications . ▁See ▁decision ▁tree . ▁ ▁As ▁compared ▁to ▁regression ▁analysis , ▁which ▁creates ▁a ▁formula ▁that ▁health ▁care ▁prov iders ▁can ▁use ▁to ▁calculate ▁the ▁probability ▁that ▁a ▁patient ▁has ▁a ▁disease , ▁recursive ▁partition ▁creates ▁a ▁rule ▁such ▁as ▁' If ▁a ▁patient ▁has ▁finding