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	मजबूत नैव बेयस	नियमितीकृत Naive Bayes
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2002	1950s–2003
प्रवर्तक	Zaffalon, M.	Good, I. J. (Laplace smoothing formalized); Rennie et al. (complement regularization)
प्रकार	Probabilistic generative classifier with imprecise-probability robustness	Probabilistic classifier with regularization
मौलिक स्रोत	Zaffalon, M. (2002). The Naive Credal Classifier. Journal of Statistical Planning and Inference, 105(1), 5–21.	Rennie, J. D. M., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of Naive Bayes text classifiers. In Proceedings of the 20th International Conference on Machine Learning (ICML-2003), pp. 616–623.
उपनाम	Naive Credal Classifier, NCC, Robust Bayesian Naive Classifier, Imprecise Naive Bayes	Smoothed Naive Bayes, Laplace-smoothed Naive Bayes, Regularized NB, Complement Naive Bayes
संबंधित	3	4
सारांश	Robust Naive Bayes extends the standard Naive Bayes classifier to handle uncertainty or noise in class-conditional probability estimates by replacing point probability estimates with intervals or sets of distributions. The canonical formulation — the Naive Credal Classifier proposed by Zaffalon (2002) — uses imprecise-probability sets so that predictions are made only when all distributions in the set agree, withholding a label when evidence is insufficient.	Regularized Naive Bayes augments the classical Naive Bayes probabilistic classifier with explicit smoothing or shrinkage — most commonly Laplace (additive) smoothing — to prevent zero-probability estimates for unseen feature values and to reduce overfitting. The result is a fast, robust classifier that generalizes better than unsmoothed Naive Bayes, particularly on sparse or high-dimensional data such as text.