Machine learning

Gradient Boosting

Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.

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Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI: 10.1214/aos/1013203451 ↗

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Active Learning Gradient Boosting Active Learning LightGBM Bagging Bayesian Boosting Bayesian LightGBM Bayesian XGBoost Boosting Boosting Ensemble Conformal Prediction (Time Series)Explainable Extra Trees Explainable Gradient Boosting Explainable LightGBM Explainable Random Forest Explainable Stacking Ensemble Explainable XGBoost Extra Trees Linear Regression (ML)MARS Online Bagging Online Boosting Online Gradient Boosting Online LightGBM Regularized Boosting Regularized CatBoost Regularized Gradient Boosting Regularized LightGBM Robust Boosting Robust Gradient Boosting Robust LightGBM Robust Random Forest Robust Stacking Ensemble Robust XGBoost Self-supervised Decision Tree Self-supervised Gradient Boosting Self-supervised LightGBM Semi-supervised Bagging Semi-supervised Boosting Semi-supervised CatBoost Semi-supervised Decision Tree Semi-supervised Gradient Boosting Semi-supervised Random Forest Semi-supervised Stacking Ensemble Semi-supervised XGBoost XGBoost

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