771: Gradient Boosting: XGBoost, LightGBM and CatBoost, with Kirill Eremenko
Super Data Science: ML & AI Podcast with Jon Krohn
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Evolution and Mathematics Behind Gradient Boosting
The chapter explores the history of boosting algorithms, focusing on gradient boosting and its development from Adaboost. It explains the iterative process of building models to predict errors from previous models, gradually improving accuracy by learning from mistakes. The episode also delves into the underlying mathematics of gradient boosting, discussing loss functions, gradients, and the evolution of advanced variants like XGBoost and LightGBM.
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