Bandingkan metode

	Pohon Ekstra	Pohon Keputusan	Random Forest
Bidang	Pembelajaran Mesin	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning	Machine learning
Tahun asal	2006	1984	2001
Pencetus	Geurts, P.; Ernst, D.; Wehenkel, L.	Breiman, Friedman, Olshen & Stone	Breiman, L.
Tipe	Ensemble (extremely randomized decision trees)	Recursive partitioning (if-then rules)	Ensemble (bagging of decision trees)
Sumber perintis	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.
Alias	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Terkait	5	5	4
Ringkasan	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.	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.