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	Modellkalibrering	Logistisk regression
Ämnesområde	Maskininlärning	Forskningsstatistik
Familj	Machine learning	Process / pipeline
Ursprungsår	2017	1958
Upphovsperson	Platt; Guo et al.	David Roxbee Cox
Typ	Post-hoc probability correction technique	Method
Ursprungskälla	Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Alias	Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu	logit model, binomial logistic regression, LR
Närliggande	3	3
Sammanfattning	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.