Methoden vergleichen

	Out-of-Distribution-Erkennung	Isolation Forest
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2017	2008
Urheber	Hendrycks & Gimpel	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Typ	Reliability and safety method for neural networks	Unsupervised ensemble (random partitioning trees)
Wegweisende Quelle	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.
Aliasnamen	OOD Detection, Novelty Detection, Open-Set Recognition, Dağılım Dışı Tespit	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Verwandt	3	5
Zusammenfassung	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.