Comparer des méthodes

	Détection hors distribution	Isolation Forest
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017	2008
Auteur d'origine	Hendrycks & Gimpel	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Type	Reliability and safety method for neural networks	Unsupervised ensemble (random partitioning trees)
Source fondatrice	Hendrycks, D., & Gimpel, K. (2017). A baseline for detecting misclassified and out-of-distribution examples in neural networks. International Conference on Learning Representations.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Alias	OOD Detection, Novelty Detection, Open-Set Recognition, Dağılım Dışı Tespit	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Apparentées	3	5
Résumé	Out-of-Distribution (OOD) detection is a set of techniques that identify when a deployed machine learning model receives inputs that differ significantly from its training data distribution. Introduced as a formal problem by Hendrycks and Gimpel in 2017, these methods enable models to flag unfamiliar inputs rather than silently produce unreliable predictions, making them foundational to trustworthy and safe AI deployment in 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.