Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Median Absolute Deviation (MAD) novērtējums× | Regulētā lineārā regresija (Ridge Regression)× | |
|---|---|---|
| Nozare≠ | Statistika | Mašīnmācīšanās |
| Saime≠ | Regression model | Machine learning |
| Izcelsmes gads≠ | 1974 | 1970 |
| Autors≠ | Hampel (influence-curve treatment); classical robust statistics | Hoerl, A.E. & Kennard, R.W. |
| Tips≠ | Robust scale estimator | L2-regularized linear regression |
| Pirmavots≠ | Hampel, F. R. (1974). The Influence Curve and Its Role in Robust Estimation. Journal of the American Statistical Association, 69(346), 383-393. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Citi nosaukumi | median absolute deviation, MAD scale estimator, robust scale estimation, Medyan Mutlak Sapma (MAD) Tahmini | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Median Absolute Deviation estimation is a robust measure of statistical dispersion that replaces the standard deviation when outliers are present. Rooted in the influence-curve framework formalised by Hampel (1974), it summarises the spread of a continuous variable using medians instead of means, so a single extreme value cannot distort the result. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. |
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