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About Nixon, Measuring Calibration in Deep Learning
1. The document discusses metrics for measuring calibration in machine learning models, specifically focusing on issues with the commonly used Expected Calibration Error (ECE) metric. 2. It performs experiments comparing different calibration metrics and finds that conclusions about which recalibration methods work best are significantly impacted by the choice of calibration measure used. 3. Key recommendations include conditioning the metric on class, using adaptive binning schemes, and using the L2 norm, as these approaches lead to more stable rankings of recalibration methods and better evaluate calibration.
Detailed Information
| Author: | ['José-Manuel Martin Coronado'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 14 |
| Language: | English |
| Format: | |
| Price: | FREE |
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