Decision Trees, Ensembles, and Advanced Boosting Techniques Introduction In last part, we explored Bayesian methods which rely on probability theory and the assumption of feature independence. While powerful, financial data often contains complex, non-linear interactions that simple independence assumptions cannot capture. For instance, a high P/E ratio might be bad for a utility stock but … Continue reading Mastering Tree-Based Models: From Roots to XGBoost