手法を比較

	勾配ブースティング	決定木	LightGBM	ロジスティック回帰
分野	機械学習	機械学習	機械学習	研究統計
系統	Machine learning	Machine learning	Machine learning	Process / pipeline
提唱年	2001	1984	2017	1958
提唱者	Friedman, J. H.	Breiman, Friedman, Olshen & Stone	Ke, G. et al. (Microsoft)	David Roxbee Cox
種類	Ensemble (sequential boosting of decision trees)	Recursive partitioning (if-then rules)	Gradient boosting decision tree ensemble	Method
原典	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
別名	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	logit model, binomial logistic regression, LR
関連	5	5	5	3
概要	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.