विधियों की तुलना करें

	एक्स्ट्रा ट्रीज़	बैगिंग (बूटस्ट्रैप एग्रीगेटिंग)
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2006	1996
प्रवर्तक	Geurts, P.; Ernst, D.; Wehenkel, L.	Breiman, L.
प्रकार	Ensemble (extremely randomized decision trees)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
मौलिक स्रोत	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
उपनाम	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
संबंधित	5	5
सारांश	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.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.