Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Gradient Boosting× | Gewone Kleinste Kwadraten (GKK) Regressie× | Kwantielregressie× | |
|---|---|---|---|
| Vakgebied≠ | Machine learning | Econometrie | Econometrie |
| Familie≠ | Machine learning | Regression model | Regression model |
| Jaar van ontstaan≠ | 2001 | 2019 | 1978 |
| Grondlegger≠ | Friedman, J. H. | Wooldridge (textbook treatment); classical least squares | Koenker & Bassett |
| Type≠ | Ensemble (sequential boosting of decision trees) | Linear regression | Conditional quantile regression |
| Oorspronkelijke bron≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Aliassen≠ | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Verwant | 5 | 5 | 5 |
| Samenvatting≠ | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. | 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). | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
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