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| Gradient Boosting× | Entscheidungsbaum× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2001 | 1984 |
| Urheber≠ | Friedman, J. H. | Breiman, Friedman, Olshen & Stone |
| Typ≠ | Ensemble (sequential boosting of decision trees) | Recursive partitioning (if-then rules) |
| Wegweisende Quelle≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Aliasnamen≠ | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | 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. |
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