The CART model is primarily used for which of the following?

The CART model, which stands for Classification and Regression Trees, is a versatile decision tree algorithm that can be utilized for both classification and regression tasks. This dual capability is one of the defining features of CART.

In classification tasks, the model is designed to predict categorical outcomes. It constructs a tree where each branch represents a possible decision or outcome based on the input features, ultimately leading to discrete class labels at the leaves of the tree. This makes it effective for problems where the goal is to categorize data points into distinct classes.

On the other hand, in regression tasks, CART serves to predict continuous outcomes. Instead of class labels, the leaves of the tree represent predicted numerical values. The algorithm divides the input space into regions with constant predicted values, allowing it to capture relationships within the data that may be linear or nonlinear.

The flexibility of the CART model to handle both types of tasks is a significant advantage, making it widely used in various domains, such as finance, healthcare, and marketing, for solving diverse predictive modeling challenges.