Vertaile menetelmiä

	Logistinen regressio	Random Forest	Pinottava yleistys (Stacking)
Tieteenala	Tutkimuksen tilastomenetelmät	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Process / pipeline	Machine learning	Machine learning
Syntyvuosi	1958	2001	1992
Kehittäjä	David Roxbee Cox	Breiman, L.	Wolpert, D.H.
Tyyppi	Method	Ensemble (bagging of decision trees)	Ensemble (heterogeneous meta-learning)
Alkuperäislähde	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.
Rinnakkaisnimet	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
Liittyvät	3	4	5
Tiivistelmä	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.