A Comprehensive Guide to Decision Trees: Theory, Applications, and Best Practices
Decision trees are a versatile, interpretable machine learning algorithm that mirrors human decision-making through hierarchical conditional splits. Widely used for classification and regression tasks, they excel in scenarios requiring transparency and explainability. This article delves into the mathematical foundations, implementation strategies, and advanced considerations for practitioners. Key Characteristics Interpretability Transparent rule-based structure ideal for regulated industries (e.g., healthcare, finance). Enables feature importance analysis via split criteria. Non-Parametric Flexibility No assumptions about data distribution. Handles mixed data types (numeric, categorical) with minimal preprocessing. Multi-Purpose Utility ...