방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 서포트 벡터 회귀× | 라쏘 회귀× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2004 | 1996 |
| 창시자≠ | Smola, A.J. & Schölkopf, B. | Tibshirani, R. |
| 유형≠ | Kernel-based supervised model (epsilon-insensitive regression) | Regularized linear regression (L1 penalty) |
| 원전≠ | Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| 별칭 | Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| 관련 | 4 | 4 |
| 요약≠ | Support Vector Regression (SVR), described in Smola and Schölkopf's 2004 tutorial, predicts a continuous outcome by fitting a function that stays within an epsilon-wide tube around the data while incurring as little error as possible. It extends the support vector machine idea from classification to regression, using a kernel to capture nonlinear relationships. | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. |
| ScholarGate데이터셋 ↗ |
|
|