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| 다변량 적응 회귀 스플라인 (Multivariate Adaptive Regression Splines, MARS)× | 결정 트리× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1991 | 1984 |
| 창시자≠ | Jerome H. Friedman | Breiman, Friedman, Olshen & Stone |
| 유형≠ | Adaptive piecewise-linear regression | Recursive partitioning (if-then rules) |
| 원전≠ | Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| 별칭≠ | multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| 관련≠ | 4 | 5 |
| 요약≠ | Multivariate adaptive regression splines, introduced by Jerome Friedman in 1991, is a flexible nonparametric regression method that automatically models nonlinearities and interactions by combining piecewise-linear 'hinge' functions. It builds the model in a forward stagewise pass that adds basis functions where they help most, then prunes back the overgrown model, yielding an interpretable additive-plus-interaction form that adapts its complexity to the data. | 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. |
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