Порівняння методів

	AdaBoost	Логістична регресія	Випадковий ліс	Stacking
Галузь	Машинне навчання	Статистика досліджень	Машинне навчання	Машинне навчання
Родина	Machine learning	Process / pipeline	Machine learning	Machine learning
Рік появи	1997	1958	2001	1992
Автор методу	Freund, Y. & Schapire, R.E.	David Roxbee Cox	Breiman, L.	Wolpert, D.H.
Тип	Ensemble (sequential boosting of weak learners)	Method	Ensemble (bagging of decision trees)	Ensemble (heterogeneous meta-learning)
Основоположне джерело	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.	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.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Інші назви	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	logit model, binomial logistic regression, LR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Пов'язані	5	3	4	5
Підсумок	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	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.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.