เปรียบเทียบวิธี

	Isolation Forest	การปรับเทียบโมเดล	การวัดปริมาณความไม่แน่นอน
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง	การจำลอง
ตระกูล	Machine learning	Machine learning	Process / pipeline
ปีกำเนิด	2008	2017	Seminal modern form: 2002
ผู้ริเริ่ม	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Platt; Guo et al.	Norbert Wiener (polynomial chaos, 1938); extended to Wiener–Askey scheme by Xiu & Karniadakis (2002)
ประเภท	Unsupervised ensemble (random partitioning trees)	Post-hoc probability correction technique	Computational uncertainty analysis framework
แหล่งต้นตำรับ	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.	Xiu, D. & Karniadakis, G.E. (2002). The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations. SIAM Journal on Scientific Computing, 24(2), 619–644.
ชื่อเรียกอื่น	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu	UQ, polynomial chaos expansion, PCE, Kriging surrogate
ที่เกี่ยวข้อง	5	3	9
สรุป	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.	Uncertainty Quantification (UQ) is a computational framework for systematically measuring how uncertainty in the inputs of a model propagates into uncertainty in its outputs. Building on Wiener's polynomial chaos theory (1938) and formalised for general stochastic problems by Xiu and Karniadakis (2002), UQ uses two primary strategies: Polynomial Chaos Expansion (PCE), which represents the model output as a series of orthogonal polynomials matched to the input distributions, and Kriging (Gaussian process) surrogates, which replace an expensive simulation with a fast statistical approximation fitted to a small set of carefully chosen runs.