Сравнение методов

	Isolation Forest	Логистическая регрессия	Случайный лес
Область	Машинное обучение	Статистика исследований	Машинное обучение
Семейство	Machine learning	Process / pipeline	Machine learning
Год появления	2008	1958	2001
Автор метода	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	David Roxbee Cox	Breiman, L.
Тип	Unsupervised ensemble (random partitioning trees)	Method	Ensemble (bagging of decision trees)
Основополагающий источник	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Другие названия	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	logit model, binomial logistic regression, LR	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	5	3	4
Сводка	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.	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.