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| Konformalne przewidywanie× | Regresja logistyczna× | |
|---|---|---|
| Dziedzina≠ | Uczenie maszynowe | Statystyka w badaniach |
| Rodzina≠ | Machine learning | Process / pipeline |
| Rok powstania≠ | 2005 | 1958 |
| Twórca≠ | Vovk, Gammerman & Shafer | David Roxbee Cox |
| Typ≠ | Distribution-free uncertainty quantification framework | Method |
| Źródło pierwotne≠ | Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic Learning in a Random World. Springer. ISBN: 978-0-387-00152-4 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Inne nazwy≠ | Conformal Inference, Conformal Risk Control, Inductive Conformal Prediction, Uyumsal Tahmin | logit model, binomial logistic regression, LR |
| Pokrewne≠ | 2 | 3 |
| Podsumowanie≠ | 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. | 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. |
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