Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Kwantielregressie (Niet-parametrische varianten)× | Lasso-regressie× | |
|---|---|---|
| Vakgebied≠ | Statistiek | Machine learning |
| Familie≠ | Regression model | Machine learning |
| Jaar van ontstaan≠ | 1978 | 1996 |
| Grondlegger≠ | Koenker & Bassett | Tibshirani, R. |
| Type≠ | Quantile regression (nonparametric variants) | Regularized linear regression (L1 penalty) |
| Oorspronkelijke bron≠ | Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| Aliassen | quantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar) | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| Verwant≠ | 5 | 4 |
| Samenvatting≠ | Quantile regression, introduced by Koenker and Bassett in 1978, models a chosen conditional quantile (such as the median or the 25th and 75th percentiles) of a continuous outcome rather than its mean. Its nonparametric variants fit these quantile relationships without assuming a distribution for the errors, making them a robust complement to mean-based regression on skewed data. | 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. |
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