So sánh phương pháp

	Dự đoán Hợp thức	Hiệu chỉnh mô hình
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2005	2017
Người khởi xướng	Vovk, Gammerman & Shafer	Platt; Guo et al.
Loại	Distribution-free uncertainty quantification framework	Post-hoc probability correction technique
Công trình gốc	Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic Learning in a Random World. Springer. ISBN: 978-0-387-00152-4	Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330.
Tên gọi khác	Conformal Inference, Conformal Risk Control, Inductive Conformal Prediction, Uyumsal Tahmin	Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu
Liên quan	2	3
Tóm tắt	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.	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.