Comparer des méthodes

	Isolation Forest	Calage du modèle
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2008	2017
Auteur d'origine	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Platt; Guo et al.
Type	Unsupervised ensemble (random partitioning trees)	Post-hoc probability correction technique
Source fondatrice	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330.
Alias	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu
Apparentées	5	3
Résumé	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.	Model calibration is a post-hoc technique that adjusts the probability outputs of a trained classifier so that predicted confidence scores match empirical outcome frequencies. A classifier is said to be perfectly calibrated if, among all predictions made with confidence p, exactly a fraction p of them are correct. Systematic miscalibration of modern deep neural networks was rigorously documented by Guo et al. (2017), who showed that networks trained with standard cross-entropy loss tend to be overconfident, and proposed temperature scaling as a simple, effective remedy.