https://github.com/google/TrustScore

To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective than the classifier's own implied confidence (e.g. softmax probability for a neural network).
https://github.com/google/TrustScore

uncertainty-estimation uncertainty-neural-networks

Last synced: 9 days ago
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To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective than the classifier's own implied confidence (e.g. softmax probability for a neural network).

• Host: GitHub
• URL: https://github.com/google/TrustScore
• Owner: google
• License: apache-2.0
• Archived: true
• Created: 2018-09-03T16:23:29.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2023-03-23T18:24:45.000Z (over 1 year ago)
• Last Synced: 2024-08-02T15:18:45.698Z (3 months ago)
• Topics: uncertainty-estimation, uncertainty-neural-networks
• Language: Jupyter Notebook
• Homepage: https://arxiv.org/abs/1805.11783
• Size: 266 KB
• Stars: 173
• Watchers: 13
• Forks: 42
• Open Issues: 2
• Metadata Files:
  • Readme: README.rst
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
To Trust Or Not To Trust A Classifier

======

This is not an officially supported Google product

Signal for model confidence for a trained classifier, computed based on

labeled training examples and the classifier's hard predictions on these

examples.

See https://arxiv.org/abs/1805.11783