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| Regularisierter Entscheidungsbaum× | Boosting× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1984 | 1990–1997 |
| Urheber≠ | Breiman, L., Friedman, J., Olshen, R., & Stone, C. | Schapire, R. E.; Freund, Y. |
| Typ≠ | Supervised learning (regularized tree) | Sequential ensemble (iterative reweighting) |
| Wegweisende Quelle≠ | Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8 | 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 ↗ |
| Aliasnamen | pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees. | 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. |
| ScholarGateDatensatz ↗ |
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