方法对比
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| 贝叶斯堆叠集成× | 堆叠法× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2018 | 1992 |
| 提出者≠ | Yao, Y.; Vehtari, A.; Simpson, D.; Gelman, A. | Wolpert, D.H. |
| 类型≠ | Bayesian ensemble combination | Ensemble (heterogeneous meta-learning) |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | Bayesian stacking, Bayesian model stacking, stacking with Bayesian weights, predictive distribution stacking | Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner |
| 相关≠ | 6 | 5 |
| 摘要≠ | 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. |
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