Uporedite metode

	Робусна насумична шума	Bagging (Bootstrap Aggregating)	Isolation Forest
Oblast	Mašinsko učenje	Mašinsko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning	Machine learning
Godina nastanka	2000s–2010s	1996	2008
Tvorac	Various (extensions of Breiman 2001 Random Forest)	Breiman, L.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Tip	Robust Ensemble (noise-tolerant bagging of decision trees)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Unsupervised ensemble (random partitioning trees)
Temeljni izvor	Chen, S., & Guestrin, C. (2019). Robust Random Forest. In Proceedings of the 36th International Conference on Machine Learning (ICML). Also see: Gao, W., & Zhou, Z.-H. (2013). On the Doubt about Margin Explanation of Boosting. Artificial Intelligence, 203, 1–18.	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.
Drugi nazivi	RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Srodne	6	5	5
Sažetak	Robust Random Forest extends the standard Random Forest ensemble by incorporating mechanisms that reduce the influence of outliers, label noise, and corrupted observations. Rather than treating all training instances equally, it applies weighting or filtering strategies so that noisy or anomalous samples contribute less to individual tree splits, yielding predictions that remain reliable even when data quality is imperfect.	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.