পদ্ধতির তুলনা করুন

	লজিস্টিক রিগ্রেশন	মডেল ক্যালিব্রেশন
ক্ষেত্র	গবেষণা পরিসংখ্যান	যন্ত্র শিখন
পরিবার	Process / pipeline	Machine learning
উদ্ভবের বছর	1958	2017
প্রবর্তক	David Roxbee Cox	Platt; Guo et al.
ধরন	Method	Post-hoc probability correction technique
মৌলিক উৎস	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330.
অপর নাম	logit model, binomial logistic regression, LR	Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu
সম্পর্কিত	3	3
সারসংক্ষেপ	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.	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.