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| 일반화 가법 모형 (GAM)× | 회귀 스플라인 및 스무딩 스플라인× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1986 | 1996 |
| 창시자≠ | Trevor Hastie & Robert Tibshirani | Spline regression literature; P-splines by Eilers & Marx |
| 유형≠ | Semi-parametric additive regression model | Piecewise-polynomial nonparametric regression |
| 원전≠ | Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. DOI ↗ | Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121. DOI ↗ |
| 별칭≠ | GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model | splines, cubic splines, natural splines, smoothing splines |
| 관련 | 4 | 4 |
| 요약≠ | 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. | Regression splines model a nonlinear relationship by fitting piecewise polynomials that join smoothly at a set of points called knots. Cubic and natural splines are the most common, and smoothing splines add a roughness penalty that automatically balances fit against smoothness. Splines are the standard flexible building block for univariate nonlinear regression and the basis of generalized additive models. |
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