Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Регрессия Лассо× | Регрессия по методу наименьших усеченных квадратов (LTS)× | |
|---|---|---|
| Область≠ | Машинное обучение | Статистика |
| Семейство≠ | Machine learning | Regression model |
| Год появления≠ | 1996 | 1984 |
| Автор метода≠ | Tibshirani, R. | Peter J. Rousseeuw |
| Тип≠ | Regularized linear regression (L1 penalty) | Robust linear regression |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия≠ | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | LTS, least trimmed squares regression, trimmed least squares, robust regression |
| Связанные≠ | 4 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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