Decision Tree

Decision trees measure feature importance via impurity reduction (e.g., Gini). Overfitting occurs when trees fit noise, not patterns. Pruning reduces complexity: pre-pruning uses max depth or min samples, while post-pruning, like cost complexity pruning, trims nodes after growth. These methods enhance generalization, improving performance on new data, making them vital for effective machine learning models.

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

Discover the power of decision trees in machine learning. This post dives into their intuitive approach, versatility for classification and regression, and the CART algorithm. Learn how Gini impurity and splitting criteria partition data for accurate predictions. Perfect for data science enthusiasts !