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Model Calibration

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.

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Probability Calibration of Classifiers

Taksonomisk metoderegister · ml-model / machine-learning

Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330. · URL

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Taxonomic bucketConformal Predictionmachine-suggested · Relational suggestion, not evidence.See alsoLogistic Regressionmachine-suggested · Relational suggestion, not evidence.See alsoUncertainty Quantificationmachine-suggested · Relational suggestion, not evidence.