Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Otsustuspuu× | XGBoost× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 1984 | 2016 |
| Looja≠ | Breiman, Friedman, Olshen & Stone | Chen, T. & Guestrin, C. |
| Tüüp≠ | Recursive partitioning (if-then rules) | Ensemble (gradient-boosted decision trees) |
| Algallikas≠ | 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 ↗ |
| Rööpnimetused≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | XGBoost, extreme gradient boosting, scalable tree boosting |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | 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. |
| ScholarGateAndmestik ↗ |
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