مقایسهٔ روش‌ها

	کالیبراسیون مدل	کمی‌سازی عدم قطعیت
حوزه	یادگیری ماشین	شبیه‌سازی
خانواده	Machine learning	Process / pipeline
سال پیدایش	2017	Seminal modern form: 2002
پدیدآور	Platt; Guo et al.	Norbert Wiener (polynomial chaos, 1938); extended to Wiener–Askey scheme by Xiu & Karniadakis (2002)
نوع	Post-hoc probability correction technique	Computational uncertainty analysis framework
منبع بنیادین	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.
نام‌های دیگر	Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu	UQ, polynomial chaos expansion, PCE, Kriging surrogate
مرتبط	3	9
خلاصه	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.