The U.S. Army has a system of large personnel flow models to manage the soldiers. The partitioning of the soldiers into groups having common behavior is an important aspect of such models. This thesis presents Breiman's Classification and Regression Trees (CART) as a method of studying partitions relative to loss behavior. It demonstrates that CART is a simple technique to use and understand while at the same time still being a powerful forecasting tool. A CART example is included that provides the reader a thorough understanding of the method. The analysis explores the structure found in the current Classification Groups (C-Groups) used by the Army. CART is used to review the structure of the C-Groups and conduct some exploratory work to demonstrate that different combinations of factors result in greater internal homogeneity in forecasting. Recommendations are provided on how to approach the process of modifying the C-Groups. The use of CART results in obtaining insights into the Army force structure that would not have been found with any other forecasting technique. This thesis reveals the power of CART as a forecasting tool.