Classification and Regression Trees (CART) Algorithm

The Classification and Regression Trees (CART) algorithm is a widely used decision tree method in machine learning that can handle both classification problems and regression tasks. Its core concept involves recursively partitioning the dataset into purer subsets to construct a binary tree.

The CART algorithm workflow includes the following key steps: First, it selects an optimal feature and split point that maximize the purity of the resulting child nodes (using Gini impurity or entropy for classification tasks, and variance for regression tasks). This process recurses until stopping conditions are met (such as reaching maximum depth or having insufficient samples in a node). In code implementation, this typically involves iterating through features and potential split points to find the one that minimizes impurity or variance.

CART's flexibility is demonstrated through its ability to handle both numerical and categorical features, along with inherent robustness to outliers and missing values. Algorithm implementations often include feature encoding techniques for categorical variables and imputation strategies for missing data.

In practical applications, CART's decision trees offer high interpretability with clear visualization of feature importance, making them popular in financial risk control, medical diagnosis, and other domains. The algorithm also serves as a fundamental component for ensemble methods like Random Forests and Gradient Boosted Trees, where multiple CART trees are combined to improve predictive performance through techniques such as bagging or boosting.