So sánh phương pháp

	Isolation Forest	Cây Quyết định
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2008	1984
Người khởi xướng	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Breiman, Friedman, Olshen & Stone
Loại	Unsupervised ensemble (random partitioning trees)	Recursive partitioning (if-then rules)
Công trình gốc	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Tên gọi khác	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Liên quan	5	5
Tóm tắt	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.	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.