Salīdzināt metodes

	Extra Trees	Koku lēmumu pieņemšana (Decision Tree)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2006	1984
Autors	Geurts, P.; Ernst, D.; Wehenkel, L.	Breiman, Friedman, Olshen & Stone
Tips	Ensemble (extremely randomized decision trees)	Recursive partitioning (if-then rules)
Pirmavots	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.
Citi nosaukumi	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Saistītās	5	5
Kopsavilkums	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.