Comparar métodos

	Regresión Logística	Calibración del modelo
Campo	Estadística para la investigación	Aprendizaje automático
Familia	Process / pipeline	Machine learning
Año de origen	1958	2017
Autor original	David Roxbee Cox	Platt; Guo et al.
Tipo	Method	Post-hoc probability correction technique
Fuente seminal	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.
Alias	logit model, binomial logistic regression, LR	Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu
Relacionados	3	3
Resumen	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.