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| 그래디언트 부스팅× | 회귀 스플라인 및 스무딩 스플라인× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2001 | 1996 |
| 창시자≠ | Friedman, J. H. | Spline regression literature; P-splines by Eilers & Marx |
| 유형≠ | Ensemble (sequential boosting of decision trees) | Piecewise-polynomial nonparametric regression |
| 원전≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121. DOI ↗ |
| 별칭≠ | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | splines, cubic splines, natural splines, smoothing splines |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. | 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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