6 Reasons to Combine CART and TreeNet:
#1 Build predictive models quickly: One advantage of CART is that is has the ability to build models relatively fast.
#2 Incorporate all types of variables: Your model can include numeric, binary, categorical, and missing values.
#3 Interpretable model representation: CART’s easy-to-understand decision tree graphics will make your job easy when explaining the model to your boss! All you have to do is print it out!
#4 Maintain model stability: One of TreeNet’s top advantages is that it will retain a stable model due to averaging of the individual decision tree responses – something difficult to do with CART.
#5 Produce a high interaction order model: TreeNet allows precise control over interactions among multiple variables.
#6 Include ALL variables: In CART, relatively few predictors make it into the model, but when using TreeNet each tree works with the entire data – many opportunities for variables to enter.
When combining TreeNet and CART – you maintain the simplicity of CART while overcoming its challenges with TreeNet gradient boosting.Read More