Krahasoni metodat

	Bagim (Agregimi Bootstrap)	Isolation Forest
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	1996	2008
Krijuesi	Breiman, L.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Lloji	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Unsupervised ensemble (random partitioning trees)
Burimi themelues	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Emërtime të tjera	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Të lidhura	5	5
Përmbledhja	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.	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.