Vertaile menetelmiä

	Gradient Boosting	Päätöspuu	Logistinen regressio
Tieteenala	Koneoppiminen	Koneoppiminen	Tutkimuksen tilastomenetelmät
Menetelmäperhe	Machine learning	Machine learning	Process / pipeline
Syntyvuosi	2001	1984	1958
Kehittäjä	Friedman, J. H.	Breiman, Friedman, Olshen & Stone	David Roxbee Cox
Tyyppi	Ensemble (sequential boosting of decision trees)	Recursive partitioning (if-then rules)	Method
Alkuperäislähde	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Rinnakkaisnimet	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	logit model, binomial logistic regression, LR
Liittyvät	5	5	3
Tiivistelmä	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.	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.