Сравнение на методи

	Isolation Forest	Случайна гора
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2008	2001
Създател	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Breiman, L.
Тип	Unsupervised ensemble (random partitioning trees)	Ensemble (bagging of decision trees)
Основополагащ източник	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.
Други названия	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Свързани	5	4
Резюме	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.