Ensemble Naive Bayes
Ensemble Naive Bayes trains multiple Naive Bayes classifiers — each exposed to a different view of the data through bagging, feature subsets, or boosting — and combines their probabilistic predictions by voting or probability averaging. The approach retains the speed and interpretability of individual Naive Bayes models while reducing variance and improving accuracy through ensemble aggregation.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler & F. Roli (Eds.), Multiple Classifier Systems (MCS 2000), Lecture Notes in Computer Science, vol. 1857, pp. 1–15. Springer. · DOI 10.1007/3-540-45014-9_1
- Lowd, D. & Domingos, P. (2005). Naive Bayes Models for Probability Estimation. In Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), pp. 529–536. ACM. · DOI 10.1145/1102351.1102418
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
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