Comparer des méthodes

	Isolation Forest Explicable	Isolation Forest
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2008 / 2017	2008
Auteur d'origine	Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer)	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Type	Anomaly detection with post-hoc explainability	Unsupervised ensemble (random partitioning trees)
Source fondatrice	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Alias	XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Apparentées	5	5
Résumé	Explainable Isolation Forest combines the Isolation Forest anomaly detection algorithm with post-hoc explainability tools — most commonly SHAP (SHapley Additive exPlanations) — to not only flag anomalous observations but also reveal which features drove each anomaly score. It bridges unsupervised anomaly detection with the interpretability demands of regulated and high-stakes domains.	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.