방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 회귀 스플라인 및 스무딩 스플라인× | 다항 회귀× | |
|---|---|---|
| 분야≠ | 머신러닝 | 통계학 |
| 계열≠ | Machine learning | Regression model |
| 기원 연도≠ | 1996 | 2012 |
| 창시자≠ | Spline regression literature; P-splines by Eilers & Marx | Montgomery, Peck & Vining (textbook treatment); classical least squares |
| 유형≠ | Piecewise-polynomial nonparametric regression | Linear regression in transformed predictors |
| 원전≠ | Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121. DOI ↗ | Montgomery, D. C., Peck, E. A. & Vining, G. G. (2012). Introduction to Linear Regression Analysis. Wiley. ISBN: 978-0470542811 |
| 별칭≠ | splines, cubic splines, natural splines, smoothing splines | polynomial least squares, curvilinear regression, Polinom Regresyonu |
| 관련 | 4 | 4 |
| 요약≠ | 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. | Polynomial regression is a regression method that models non-linear relationships by including squared and higher-degree terms of an explanatory variable, and it is a core tool of response surface analysis. As developed in Montgomery, Peck and Vining's Introduction to Linear Regression Analysis (2012), it remains linear in its parameters even though the fitted curve bends. |
| ScholarGate데이터셋 ↗ |
|
|