विधियों की तुलना करें

	आइसोलेशन फ़ॉरेस्ट	निर्णय वृक्ष
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2008	1984
प्रवर्तक	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Breiman, Friedman, Olshen & Stone
प्रकार	Unsupervised ensemble (random partitioning trees)	Recursive partitioning (if-then rules)
मौलिक स्रोत	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.
उपनाम	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
संबंधित	5	5
सारांश	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.