Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Beslutsträd× | XGBoost× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 1984 | 2016 |
| Upphovsperson≠ | Breiman, Friedman, Olshen & Stone | Chen, T. & Guestrin, C. |
| Typ≠ | Recursive partitioning (if-then rules) | Ensemble (gradient-boosted decision trees) |
| Ursprungskälla≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Alias≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | XGBoost, extreme gradient boosting, scalable tree boosting |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | 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. | 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. |
| ScholarGateDatamängd ↗ |
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