方法对比
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| 梯度提升（Gradient Boosting）× | 分位数回归× | |
|---|---|---|
| 领域≠ | 机器学习 | 计量经济学 |
| 方法族≠ | Machine learning | Regression model |
| 起源年份≠ | 2001 | 1978 |
| 提出者≠ | Friedman, J. H. | Koenker & Bassett |
| 类型≠ | Ensemble (sequential boosting of decision trees) | Conditional quantile regression |
| 开创性文献≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| 别名≠ | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | conditional quantile regression, regression quantiles, Kantil Regresyon |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | 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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