Confronta i metodi

	Albero decisionale	Alberi Extra	Random Forest
Campo	Apprendimento automatico	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	1984	2006	2001
Ideatore	Breiman, Friedman, Olshen & Stone	Geurts, P.; Ernst, D.; Wehenkel, L.	Breiman, L.
Tipo	Recursive partitioning (if-then rules)	Ensemble (extremely randomized decision trees)	Ensemble (bagging of decision trees)
Fonte seminale	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Correlati	5	5	4
Sintesi	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.	Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time.	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.