Võrdle meetodeid

	Bagging (Bootstrap Aggregating)	Otsustuspuu	Isolation Forest
Valdkond	Masinõpe	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	1996	1984	2008
Looja	Breiman, L.	Breiman, Friedman, Olshen & Stone	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Tüüp	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Recursive partitioning (if-then rules)	Unsupervised ensemble (random partitioning trees)
Algallikas	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Rööpnimetused	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Seotud	5	5	5
Kokkuvõte	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.	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.	Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.