Сравнение методов

	Градиентный бустинг	LightGBM	Логистическая регрессия	Случайный лес
Область	Машинное обучение	Машинное обучение	Статистика исследований	Машинное обучение
Семейство	Machine learning	Machine learning	Process / pipeline	Machine learning
Год появления	2001	2017	1958	2001
Автор метода	Friedman, J. H.	Ke, G. et al. (Microsoft)	David Roxbee Cox	Breiman, L.
Тип	Ensemble (sequential boosting of decision trees)	Gradient boosting decision tree ensemble	Method	Ensemble (bagging of decision trees)
Основополагающий источник	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Другие названия	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	logit model, binomial logistic regression, LR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	5	5	3	4
Сводка	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.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.