विधियों की तुलना करें

	व्याख्या योग्य निर्णय वृक्ष	लॉजिस्टिक रिग्रेशन
क्षेत्र	मशीन अधिगम	अनुसंधान सांख्यिकी
परिवार	Machine learning	Process / pipeline
उद्भव वर्ष	1984 (CART); XAI framing formalized 2010s–2020s	1958
प्रवर्तक	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.	David Roxbee Cox
प्रकार	Interpretable supervised learning model	Method
मौलिक स्रोत	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.
उपनाम	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree	logit model, binomial logistic regression, LR
संबंधित	4	3
सारांश	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.