For those new to CART, it is a tree-based algorithm that works by looking at many various ways to locally partition or split data into smaller segments based on differing values and combinations of predictors. CART selects the best performing splits, then repeats this process recursively until the optimal collection is found. The result is a decision tree represented by a series of binary splits leading to terminal nodes that can be described by a set of specific rules. The tree and its layout is visually stimulating and intuitive to interpret so you don’t have to be a data scientist to understand and gain useful insights from it. Designed for users of all levels, CART’s model can quickly reveal important relationships that could remain hidden when using other analytical tools. CART stands out in the predictive analytics field thanks to its original, highly desirable methodology that includes built-in automation, ease-of-use, performance, and accuracy.