Classification and Regression Tree (CART) models are a popular method in the field of machine learning for both classification and regression tasks. In R, the “rpart” package is commonly used to build CART models.

The first step in using a CART model is to prepare your data. This includes cleaning and preprocessing the data, as well as splitting it into a training set and a test set.

Next, the “rpart” function is used to build the model. The function takes several parameters such as the type of model (classification or regression), the variables to be used in the model, and any specific model tuning parameters.

Once the model is built, it can be visualized using a tree diagram, which shows the various decisions or splits that the model makes to classify or predict the target variable.

In order to evaluate the model’s performance, a number of metrics can be used such as accuracy, precision and recall for classification tasks, and R-squared for regression tasks.

Finally, the model can be used to make predictions on new, unseen data. It is important to keep in mind that CART models are sensitive to small changes in the data, so it may be necessary to re-build and re-evaluate the model periodically.