Stable reliability diagrams for probabilistic classifiers
A probability forecast or probabilistic classifier is reliable or calibrated if the predicted probabilities are matched by ex post observed frequencies, as examined visually in reliability diagrams. The classical binning and counting approach to plotting reliability diagrams has been hampered by a l...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
February 17, 2021
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| In: |
Proceedings of the National Academy of Sciences of the United States of America
Year: 2021, Volume: 118, Issue: 8, Pages: 1-10 |
| ISSN: | 1091-6490 |
| DOI: | 10.1073/pnas.2016191118 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1073/pnas.2016191118 Verlag, lizenzpflichtig, Volltext: https://www.pnas.org/content/118/8/e2016191118 
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| Author Notes: | Timo Dimitriadis, Tilmann Gneiting, and Alexander I. Jordan |
| Summary: | A probability forecast or probabilistic classifier is reliable or calibrated if the predicted probabilities are matched by ex post observed frequencies, as examined visually in reliability diagrams. The classical binning and counting approach to plotting reliability diagrams has been hampered by a lack of stability under unavoidable, ad hoc implementation decisions. Here, we introduce the CORP approach, which generates provably statistically consistent, optimally binned, and reproducible reliability diagrams in an automated way. CORP is based on nonparametric isotonic regression and implemented via the pool-adjacent-violators (PAV) algorithm—essentially, the CORP reliability diagram shows the graph of the PAV-(re)calibrated forecast probabilities. The CORP approach allows for uncertainty quantification via either resampling techniques or asymptotic theory, furnishes a numerical measure of miscalibration, and provides a CORP-based Brier-score decomposition that generalizes to any proper scoring rule. We anticipate that judicious uses of the PAV algorithm yield improved tools for diagnostics and inference for a very wide range of statistical and machine learning methods. |
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| Item Description: | Gesehen am 07.04.2021 |
| Physical Description: | Online Resource |
| ISSN: | 1091-6490 |
| DOI: | 10.1073/pnas.2016191118 |