Сравнение на методи

	Isolation Forest	Калибриране на модела
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2008	2017
Създател	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Platt; Guo et al.
Тип	Unsupervised ensemble (random partitioning trees)	Post-hoc probability correction technique
Основополагащ източник	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.
Други названия	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu
Свързани	5	3
Резюме	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.