Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Bayesian Stacking Ensemble× | Stacking× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2018 | 1992 |
| Upphovsperson≠ | Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A. | Wolpert, D.H. |
| Typ≠ | Bayesian ensemble combination | Ensemble (heterogeneous meta-learning) |
| Ursprungskälla≠ | Yao, Y., Vehtari, A., Simpson, D., & Gelman, A. (2018). Using stacking to average Bayesian predictive distributions. Bayesian Analysis, 13(3), 917–1007. DOI ↗ | Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259. DOI ↗ |
| Alias≠ | Bayesian stacking, Bayesian model stacking, stacking with Bayesian weights, predictive distribution stacking | Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner |
| Närliggande≠ | 6 | 5 |
| Sammanfattning≠ | Bayesian stacking combines the predictive distributions of several base models by finding non-negative weights that maximise the leave-one-out log predictive score of the mixture. Formalised by Yao, Vehtari, Simpson, and Gelman (2018), it yields a single calibrated predictive distribution that is provably at least as good as any single constituent model under cross-validation. | 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. |
| ScholarGateDatamängd ↗ |
|
|