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| 模型校准× | 保形预测× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017 | 2005 |
| 提出者≠ | Platt; Guo et al. | Vovk, Gammerman & Shafer |
| 类型≠ | Post-hoc probability correction technique | Distribution-free uncertainty quantification framework |
| 开创性文献≠ | Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330. link ↗ | Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic Learning in a Random World. Springer. ISBN: 978-0-387-00152-4 |
| 别名 | Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu | Conformal Inference, Conformal Risk Control, Inductive Conformal Prediction, Uyumsal Tahmin |
| 相关≠ | 3 | 2 |
| 摘要≠ | 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. | 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. |
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