Boosting Techniques Explained
Kirill introduces the concept of model zero as the average, setting the stage for understanding decision trees. Jon elaborates on the differences between random forests, which create decision trees randomly, and AdaBoost, which iteratively focuses on misclassified data points. The discussion culminates in the power of gradient boosting, emphasizing its ability to target residuals for maximum improvement.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
771: Gradient Boosting: XGBoost, LightGBM and CatBoost — with Kirill Eremenko
Related Questions