Linganisha mbinu

	Naive Bayes	Mti wa Uamuzi	Regresheni ya Logistiki	Msitu Nasibu
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine	Takwimu za Utafiti	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning	Process / pipeline	Machine learning
Mwaka wa asili	1997	1984	1958	2001
Mwanzilishi	Mitchell, T. M. (textbook treatment)	Breiman, Friedman, Olshen & Stone	David Roxbee Cox	Breiman, L.
Aina	Probabilistic classifier (Bayes' theorem with conditional independence)	Recursive partitioning (if-then rules)	Method	Ensemble (bagging of decision trees)
Chanzo asilia	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Majina mbadala	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	logit model, binomial logistic regression, LR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Zinazohusiana	4	5	3	4
Muhtasari	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.