Salīdzināt metodes

	Skaidrojams lēmumu koks	Logistiskā regresija	XGBoost
Nozare	Mašīnmācīšanās	Pētniecības statistika	Mašīnmācīšanās
Saime	Machine learning	Process / pipeline	Machine learning
Izcelsmes gads	1984 (CART); XAI framing formalized 2010s–2020s	1958	2016
Autors	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.	David Roxbee Cox	Chen, T. & Guestrin, C.
Tips	Interpretable supervised learning model	Method	Ensemble (gradient-boosted decision trees)
Pirmavots	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.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Citi nosaukumi	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree	logit model, binomial logistic regression, LR	XGBoost, extreme gradient boosting, scalable tree boosting
Saistītās	4	3	5
Kopsavilkums	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.