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| Ensemble Naive Bayes (Ensemblovani Naivni Bejz)× | Naivni Bejz× | |
|---|---|---|
| Oblast | Mašinsko učenje | Mašinsko učenje |
| Porodica | Machine learning | Machine learning |
| Godina nastanka≠ | 2000s | 1997 |
| Tvorac≠ | Various (Dietterich, T.G.; Webb, G.I.; others) | Mitchell, T. M. (textbook treatment) |
| Tip≠ | Ensemble of probabilistic classifiers | Probabilistic classifier (Bayes' theorem with conditional independence) |
| Temeljni izvor≠ | 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 ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| Drugi nazivi≠ | Bagged Naive Bayes, Boosted Naive Bayes, Naive Bayes ensemble, NB ensemble | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| Srodne≠ | 6 | 4 |
| Sažetak≠ | 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. | Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate. |
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