قارن الطرق

	التعبئة (تجميع العينات العشوائية)	غابة العزل
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	1996	2008
صاحب الطريقة	Breiman, L.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
النوع	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Unsupervised ensemble (random partitioning trees)
المصدر التأسيسي	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.
الأسماء البديلة	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
ذات صلة	5	5
الملخص	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.