This document discusses how combining CART decision trees with TreeNet tree ensembles can overcome some of the shortcomings of CART alone. It outlines the advantages of CART, including its ability to handle different variable types and its interpretability. However, it also describes CART's disadvantages like high variance and data fragmentation. TreeNet is introduced as a method that can dramatically increase accuracy by growing many weak decision trees on residuals of previous trees. Using both CART and TreeNet harnesses the interpretability of CART models with TreeNet's ability to produce more accurate and stable predictions.