Regression with classification and regression trees in R

Regression with classification and regression trees in R

Regression with classification and regression trees (CART) is a type of statistical analysis that is used to model relationships between variables. It is a decision tree-based algorithm that can be used for both classification and regression tasks. It’s a tree-based method, where the algorithm recursively splits the data into subsets based on the values of the predictor variables, and then using these subsets to make predictions.

In R, there are several packages available for building CART models, such as the ‘rpart’ and ‘tree’ packages. These packages provide functions for creating and training CART models, as well as functions for evaluating the performance of the model.

The process of building a CART model in R typically involves the following steps:

  1. Prepare the data: The first step is to prepare the data for the model. This may involve cleaning the data, splitting it into training and testing sets, and scaling the variables.
  2. Define the model: The next step is to define the structure of the model, including the size of the tree and the stopping criteria.
  3. Train the model: The model is trained using the prepared data. The model will recursively split the data into subsets based on the values of the predictor variables.
  4. Evaluate the model: The model’s performance is evaluated using various metrics such as accuracy, precision, recall, and F1 score.
  5. Make predictions: Once the model is trained and evaluated, it can be used to make predictions on new data.


CART is a powerful algorithm for both classification and regression problems, it’s simple to understand and interpret, it’s also fast for large data sets and can handle categorical variables as well. It’s particularly useful when you have multiple variables and complex relationships between them, it’s also good in dealing with missing values.


In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Regression with classification and regression trees in R.

Regression with classification and regression trees in R

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