방법 비교

	배깅 (Bootstrap Aggregating)	결정 트리	Isolation Forest
분야	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	1996	1984	2008
창시자	Breiman, L.	Breiman, Friedman, Olshen & Stone	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
유형	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Recursive partitioning (if-then rules)	Unsupervised ensemble (random partitioning trees)
원전	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.
별칭	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
관련	5	5	5
요약	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.