Сравнение методов

	Дерево решений	LightGBM	Логистическая регрессия
Область	Машинное обучение	Машинное обучение	Статистика исследований
Семейство	Machine learning	Machine learning	Process / pipeline
Год появления	1984	2017	1958
Автор метода	Breiman, Friedman, Olshen & Stone	Ke, G. et al. (Microsoft)	David Roxbee Cox
Тип	Recursive partitioning (if-then rules)	Gradient boosting decision tree ensemble	Method
Основополагающий источник	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.
Другие названия	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
Связанные	5	5	3
Сводка	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.