방법 비교

	로버스트 랜덤 포레스트	그래디언트 부스팅	Isolation Forest
분야	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	2000s–2010s	2001	2008
창시자	Various (extensions of Breiman 2001 Random Forest)	Friedman, J. H.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
유형	Robust Ensemble (noise-tolerant bagging of decision trees)	Ensemble (sequential boosting of decision trees)	Unsupervised ensemble (random partitioning trees)
원전	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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
별칭	RRF, noise-robust random forest, outlier-resistant random forest, robust ensemble forest	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
관련	6	5	5
요약	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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.