Difference Between Boosting Trees: Updates to Classics With CatBoost, XGBoost and LightGBM
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This publication discusses the differences between popular boosting tree algorithms such as CatBoost, XGBoost, and LightGBM. It covers the historical development of boosting techniques, starting with AdaBoost, and moving on to Gradient Boosting Machines (GBM), XGBoost, LightGBM, and CatBoost. Each algorithm has unique features and strengths, with CatBoost excelling in handling categorical features, XGBoost offering high performance and regularization, and LightGBM focusing on speed and efficiency. The choice of the algorithm depends on the problem and dataset, and it's recommended to try all three to find the best fit.