Vertaile menetelmiä

	Päätöspuu	Yleistetty additiivinen malli (GAM)
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1984	1986
Kehittäjä	Breiman, Friedman, Olshen & Stone	Trevor Hastie & Robert Tibshirani
Tyyppi	Recursive partitioning (if-then rules)	Semi-parametric additive regression model
Alkuperäislähde	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.
Rinnakkaisnimet	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model
Liittyvät	5	4
Tiivistelmä	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.	A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response.