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| XGBoost× | Beslutningstræ× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2016 | 1984 |
| Ophavsperson≠ | Chen, T. & Guestrin, C. | Breiman, Friedman, Olshen & Stone |
| Type≠ | Ensemble (gradient-boosted decision trees) | Recursive partitioning (if-then rules) |
| Oprindelig kilde≠ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Aliasser≠ | XGBoost, extreme gradient boosting, scalable tree boosting | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Relaterede | 5 | 5 |
| Resumé≠ | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. |
| ScholarGateDatasæt ↗ |
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