CART, or Classification and Regression Trees, is a decision tree technique used in data mining and machine learning. It helps in making predictions by splitting data into branches based on specific criteria. Each branch represents a decision point, leading to a final outcome or classification. CART can handle both categorical and continuous data, making it versatile for various applications. The CART algorithm works by recursively dividing the dataset into subsets that are more homogeneous. It uses measures like Gini impurity for classification tasks and mean squared error for regression tasks. This method is popular for its simplicity and interpretability, allowing users to visualize decision-making processes easily.