Dive into Decision Trees for Regression (CART), understanding its core mechanics for continuous target variables. This post covers how CART evaluates splits using Mean Squared Error (MSE), its geometric interpretation of creating axis-aligned regions, and the step-by-step process of making predictions for both regression and classification tasks. Discover its advantages in handling non-linear data and key disadvantages like overfitting, emphasizing the need for regularization