השוואת שיטות

	יער בידוד	יער אקראי
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2008	2001
הוגה השיטה	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Breiman, L.
סוג	Unsupervised ensemble (random partitioning trees)	Ensemble (bagging of decision trees)
מקור מכונן	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
כינויים	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
קשורות	5	4
תקציר	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.