Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| AdaBoost× | Beslutningstre× | Stacking× | |
|---|---|---|---|
| Fagfelt | Maskinlæring | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning | Machine learning |
| Opprinnelsesår≠ | 1997 | 1984 | 1992 |
| Opphavsperson≠ | Freund, Y. & Schapire, R.E. | Breiman, Friedman, Olshen & Stone | Wolpert, D.H. |
| Type≠ | Ensemble (sequential boosting of weak learners) | Recursive partitioning (if-then rules) | Ensemble (heterogeneous meta-learning) |
| Opprinnelig kilde≠ | 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 ↗ | Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259. DOI ↗ |
| Alias≠ | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner |
| Relaterte | 5 | 5 | 5 |
| Sammendrag≠ | AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification. | 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. | Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions. |
| ScholarGateDatasett ↗ |
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