Machine Learning for Quants Series with Python (Part 9) Introduction In Part 8, we explored parallel ensemble methods. In Bagging and Stacking, our base models are largely independent; the SVM doesn’t know or care what the Decision Tree is doing. Boosting flips this paradigm entirely. Boosting is a sequential process. It builds a model, evaluates … Continue reading Sequential Mastery: AdaBoost, Gradient Boosting, and Pseudo-Residuals