Regression with CART Trees

Classification and Regression Trees (CART) are a non-parametric method used for both classification and regression tasks. This text specifically focuses on using CART for regression, aiming to predict continuous output variables. The CART algorithm builds binary trees by repeatedly splitting the dataset based on input variables and split points. The splitting process continues until a terminal node is reached, dividing the data into subsets. Feature selection is crucial, employing a greedy algorithm to find the best input variable and split point. Binary splitting divides the data into two child nodes based on the selected feature. The tree-building process is recursive, stopping when a pre-defined criterion is met, like a minimum sample size or maximum depth. Tree pruning is performed after the full tree is built to remove branches that do not improve prediction accuracy. CART's versatility allows for its application in diverse areas due to its ability to handle both classifications and regression problems. In healthcare, CART is used for predicting disease likelihood and post-operative complications. In finance, CART is used for assessing customer creditworthiness based on various financial variables.