Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Parastā mazāko kvadrātu (OLS) regresija× | LASSO regresija× | |
|---|---|---|
| Nozare≠ | Ekonometrija | Mašīnmācīšanās |
| Saime≠ | Regression model | Machine learning |
| Izcelsmes gads≠ | 2019 | 1996 |
| Autors≠ | Wooldridge (textbook treatment); classical least squares | Tibshirani, R. |
| Tips≠ | Linear regression | Regularized linear regression (L1 penalty) |
| Pirmavots≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| Citi nosaukumi | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | 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). | 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. |
| ScholarGateDatu kopa ↗ |
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