Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Päätöspuu× | Gradient Boosting× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 1984 | 2001 |
| Kehittäjä≠ | Breiman, Friedman, Olshen & Stone | Friedman, J. H. |
| Tyyppi≠ | Recursive partitioning (if-then rules) | Ensemble (sequential boosting of decision trees) |
| Alkuperäislähde≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Rinnakkaisnimet≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. | 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. |
| ScholarGateAineisto ↗ |
|
|