Σύγκριση μεθόδων

	Επεξηγήσιμο Δέντρο Απόφασης	Τυχαίο Δάσος
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1984 (CART); XAI framing formalized 2010s–2020s	2001
Δημιουργός	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.	Breiman, L.
Τύπος	Interpretable supervised learning model	Ensemble (bagging of decision trees)
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	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Εναλλακτικές ονομασίες	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Συναφείς	4	4
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.	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.