Salīdzināt metodes

	Modeļa kalibrēšana	Konformālā prognozēšana	Nenoteiktības kvantifikācija
Nozare	Mašīnmācīšanās	Mašīnmācīšanās	Simulācija
Saime	Machine learning	Machine learning	Process / pipeline
Izcelsmes gads	2017	2005	Seminal modern form: 2002
Autors	Platt; Guo et al.	Vovk, Gammerman & Shafer	Norbert Wiener (polynomial chaos, 1938); extended to Wiener–Askey scheme by Xiu & Karniadakis (2002)
Tips	Post-hoc probability correction technique	Distribution-free uncertainty quantification framework	Computational uncertainty analysis framework
Pirmavots	Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330.	Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic Learning in a Random World. Springer. ISBN: 978-0-387-00152-4	Xiu, D. & Karniadakis, G.E. (2002). The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations. SIAM Journal on Scientific Computing, 24(2), 619–644.
Citi nosaukumi	Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu	Conformal Inference, Conformal Risk Control, Inductive Conformal Prediction, Uyumsal Tahmin	UQ, polynomial chaos expansion, PCE, Kriging surrogate
Saistītās	3	2	9
Kopsavilkums	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.	Conformal Prediction is a distribution-free framework for constructing statistically valid prediction sets (for classification) or prediction intervals (for regression) around the output of any pre-trained machine learning model. Introduced by Vovk, Gammerman, and Shafer in their 2005 monograph, it provides a finite-sample marginal coverage guarantee — the true label falls inside the prediction set with at least 1-alpha probability — without requiring parametric assumptions about the data distribution.	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.