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CART (Classification And Regression Tree) in Machine Learning - GeeksforGeeks

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CART( Classification And Regression Trees) is a variation of the decision tree algorithm. It can handle both classification and regression tasks. Scikit-Learn uses the Classification And Regression Tree (CART) algorithm to train Decision Trees (also called “growing” trees). CART was first produced by Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone in 1984. CART is a predictive algorithm used in Machine learning and it explains how the target variable's values can be predicted based on other matters. It is a decision tree where each fork is split into a predictor variable and each node has a prediction for the target variable at the end. The term CART serves as a generic term for the following categories of decision trees: In the decision tree, nodes are split into sub-nodes based on a threshold value of an attribute. The root node is taken as the training set and is split into two by considering the best attribute and threshold value. Further, the subsets are also spl

CART (Classification And Regression Tree) in Machine Learning - GeeksforGeeks Courses Tutorials Interview Prep Python for Machine Learning Machine Learning with R Machine Learning Algorithms EDA Math for Machine Learning Machine Learning Interview Questions ML Projects Deep Learning NLP Computer vision Data Science Artificial Intelligence CART (Classification And Regression Tree) in Machine Learning Last Updated : 4 Dec, 2025 To break a dataset into smaller, meaningful groups, CART (Classification and Regression Tree) is used which builds a decision tree that predicts outcomes for both classif

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