Vertaile menetelmiä

	Regularisoitu Naive Bayes	Logistinen regressio
Tieteenala	Koneoppiminen	Tutkimuksen tilastomenetelmät
Menetelmäperhe	Machine learning	Process / pipeline
Syntyvuosi	1950s–2003	1958
Kehittäjä	Good, I. J. (Laplace smoothing formalized); Rennie et al. (complement regularization)	David Roxbee Cox
Tyyppi	Probabilistic classifier with regularization	Method
Alkuperäislähde	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Rinnakkaisnimet	Smoothed Naive Bayes, Laplace-smoothed Naive Bayes, Regularized NB, Complement Naive Bayes	logit model, binomial logistic regression, LR
Liittyvät	4	3
Tiivistelmä	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.