手法を比較

	ベイズ的XGBoost	勾配ブースティング	LightGBM	XGBoost
分野	機械学習	機械学習	機械学習	機械学習
系統	Machine learning	Machine learning	Machine learning	Machine learning
提唱年	2012–2016	2001	2017	2016
提唱者	Chen, T. & Guestrin, C. (XGBoost); Snoek, J. et al. (Bayesian Optimization)	Friedman, J. H.	Ke, G. et al. (Microsoft)	Chen, T. & Guestrin, C.
種類	Ensemble (gradient boosted trees with Bayesian hyperparameter search)	Ensemble (sequential boosting of decision trees)	Gradient boosting decision tree ensemble	Ensemble (gradient-boosted decision trees)
原典	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
別名	Bayesian XGBoost, XGBoost with Bayesian Optimization, BayesOpt-XGBoost, Bayes-tuned XGBoost	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	XGBoost, extreme gradient boosting, scalable tree boosting
関連	4	5	5	5
概要	Bayesian XGBoost combines the predictive power of Extreme Gradient Boosting with Bayesian optimization for hyperparameter tuning. Instead of grid or random search, a probabilistic surrogate model guides the search for optimal learning rate, tree depth, and regularization parameters, achieving near-peak performance with far fewer evaluations than exhaustive search approaches.	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.	LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.