Porovnať metódy

	Gradient Boosting	Isolation Forest
Odbor	Strojové učenie	Strojové učenie
Rodina	Machine learning	Machine learning
Rok vzniku	2001	2008
Tvorca	Friedman, J. H.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Typ	Ensemble (sequential boosting of decision trees)	Unsupervised ensemble (random partitioning trees)
Pôvodný zdroj	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.
Ďalšie názvy	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Príbuzné	5	5
Zhrnutie	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.