Methoden vergelijken

	Uitlegbare Extra Trees	Beslisboom	Random Forest
Vakgebied	Machine learning	Machine learning	Machine learning
Familie	Machine learning	Machine learning	Machine learning
Jaar van ontstaan	2006 (Extra Trees); 2017 (SHAP integration)	1984	2001
Grondlegger	Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer)	Breiman, Friedman, Olshen & Stone	Breiman, L.
Type	Ensemble (randomized trees) with post-hoc explainability	Recursive partitioning (if-then rules)	Ensemble (bagging of decision trees)
Oorspronkelijke bron	Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Aliassen	XAI-ET, Explainable ET, Interpretable Extra Trees, Extra Trees with SHAP	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Verwant	5	5	4
Samenvatting	Explainable Extra Trees combines the Extremely Randomized Trees (Extra Trees) ensemble algorithm with post-hoc explainability methods — most commonly SHAP values — to deliver both strong predictive performance and transparent, feature-level explanations. It extends the classic Extra Trees classifier or regressor so that every prediction can be decomposed into individual feature contributions, satisfying demands for accountability in applied and regulated domains.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.