قارن الطرق

	الانحدار اللوجستي	بايز الساذج (Naive Bayes)	الغابات العشوائية
المجال	إحصاء البحث	تعلم الآلة	تعلم الآلة
العائلة	Process / pipeline	Machine learning	Machine learning
سنة النشأة	1958	1997	2001
صاحب الطريقة	David Roxbee Cox	Mitchell, T. M. (textbook treatment)	Breiman, L.
النوع	Method	Probabilistic classifier (Bayes' theorem with conditional independence)	Ensemble (bagging of decision trees)
المصدر التأسيسي	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	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
الأسماء البديلة	logit model, binomial logistic regression, LR	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
ذات صلة	3	4	4
الملخص	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.	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.