Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Boosting× | Beslutsträd× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 1990–1997 | 1984 |
| Upphovsperson≠ | Schapire, R. E.; Freund, Y. | Breiman, Friedman, Olshen & Stone |
| Typ≠ | Sequential ensemble (iterative reweighting) | Recursive partitioning (if-then rules) |
| Ursprungskälla≠ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Alias≠ | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Närliggande≠ | 6 | 5 |
| Sammanfattning≠ | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. | 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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