Multi-way Splits in Decision Trees Where The Dependent Variable Has More Than Two Levels

Russ Lavery1 and YuTing Tian2
1contractor, 2West Chester Uinversity


Abstract

Decision trees are no longer new tools of Data Scientists and are frequently used to split people into binary groups (two way splits). However SAS Enterprise Miner has the ability to create decision trees that split into more than two levels. This can be very useful if an analyst is trying to assign observations into more than two groups. This paper uses examples to explore this powerful feature (multi-way splits) of SAS Enterprise Miner to predict both n-way categorical variables and continuous variables.