Σύγκριση μεθόδων

	Λογιστική Παλινδρόμηση (ML)	Τυχαίο Δάσος
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1958	2001
Δημιουργός	Cox, D. R.	Breiman, L.
Τύπος	Probabilistic linear classifier	Ensemble (bagging of decision trees)
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	logit model, logit regression, binomial logistic regression, maximum entropy classifier	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Συναφείς	5	4
Σύνοψη	Logistic regression is a foundational probabilistic classifier that models the log-odds of a binary (or multinomial) outcome as a linear function of the predictors. Introduced by D. R. Cox in 1958, it remains one of the most widely used and interpretable classification methods in both statistics and machine learning, valued for its calibrated probability outputs and clear coefficient interpretation.	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.