方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| Lasso 回归× | 最小裁剪平方和（LTS）回归× | |
|---|---|---|
| 领域≠ | 机器学习 | 统计学 |
| 方法族≠ | Machine learning | Regression model |
| 起源年份≠ | 1996 | 1984 |
| 提出者≠ | Tibshirani, R. | Peter J. Rousseeuw |
| 类型≠ | Regularized linear regression (L1 penalty) | Robust linear regression |
| 开创性文献≠ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880. DOI ↗ |
| 别名≠ | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | LTS, least trimmed squares regression, trimmed least squares, robust regression |
| 相关≠ | 4 | 5 |
| 摘要≠ | 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. | Least Trimmed Squares is a robust linear regression method introduced by Peter J. Rousseeuw in 1984. Instead of fitting all residuals, it estimates the coefficients by minimising the sum of only the h smallest squared residuals, which gives it a breakdown point of up to 50% and reliable estimates on data heavily contaminated by outliers. |
| ScholarGate数据集 ↗ |
|
|