Σύγκριση μεθόδων

	Επεξηγήσιμο Δέντρο Απόφασης	Λογιστική Παλινδρόμηση
Πεδίο	Μηχανική Μάθηση	Ερευνητική Στατιστική
Οικογένεια	Machine learning	Process / pipeline
Έτος προέλευσης	1984 (CART); XAI framing formalized 2010s–2020s	1958
Δημιουργός	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.	David Roxbee Cox
Τύπος	Interpretable supervised learning model	Method
Θεμελιώδης πηγή	Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. ISBN: 978-0-412-04841-8	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Εναλλακτικές ονομασίες	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree	logit model, binomial logistic regression, LR
Συναφείς	4	3
Σύνοψη	An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes.	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.