Linganisha mbinu

	Regresheni ya Logistiki	Naive Bayes	Support Vector Machine (Uainishaji)
Nyanja	Takwimu za Utafiti	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Process / pipeline	Machine learning	Machine learning
Mwaka wa asili	1958	1997	1995
Mwanzilishi	David Roxbee Cox	Mitchell, T. M. (textbook treatment)	Cortes, C. & Vapnik, V.
Aina	Method	Probabilistic classifier (Bayes' theorem with conditional independence)	Maximum-margin classifier (kernel method)
Chanzo asilia	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Majina mbadala	logit model, binomial logistic regression, LR	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Zinazohusiana	3	4	5
Muhtasari	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.	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.