手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| Lasso回帰× | 最小二乗法 (OLS) 回帰× | |
|---|---|---|
| 分野≠ | 機械学習 | 計量経済学 |
| 系統≠ | Machine learning | Regression model |
| 提唱年≠ | 1996 | 2019 |
| 提唱者≠ | Tibshirani, R. | Wooldridge (textbook treatment); classical least squares |
| 種類≠ | Regularized linear regression (L1 penalty) | Linear regression |
| 原典≠ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 別名 | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 関連≠ | 4 | 5 |
| 概要≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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