Comments (4)
warenlg
commented on September 22, 2024
It turns out the identity graph of intel and IBM were pretty big: 80k and 11k edges respectively. And reducing the proportion of popular names decreased the number of false positive and false negative as popular identities tend to be the ones with problems. That's why increasing the popularity threshold from 5 to 100, we improved our precision and recall from ~62 to 94% for both organizations.
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warenlg
commented on September 22, 2024
We can not increase the popular threshold too much though otherwise we start loosing precision at some point.
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vmarkovtsev
commented on September 22, 2024
Great data analysis Waren 👍 Let's use your recommended threshold 100 and update the CSVs/embedded Go code.
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warenlg
commented on September 22, 2024
Thanks just opened the PR this morning #59
Now closed.
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Related Issues (20)
- Study how the quality depends on the hard identity size limit HOT 1
- Include the external identifiers into the result HOT 2
- Add another output format: Postgres HOT 2
- request external API only once
- Make the project open-source HOT 2
- Assume the output format is parquet when the output path points to a parquet file HOT 1
- Detect the primary name of an identified person HOT 1
- The list of popular names is too large HOT 1
- Use more efficient API for GitHub HOT 3
- Consider committer data, not just commit author HOT 3
- Debug the bot detection pipeline HOT 1
- Extract commit date for stats filtering
- panic: json: unsupported value: NaN HOT 13
- Save and load the bot detection model from modelforge HOT 6
- v3.1.0 doesn't seem to finish after running on writeas org HOT 6
- Alter Docker image so it dumps output to a defined folder HOT 4
- Print version at startup HOT 1
- Incremental operation support
- Performance dropped critically HOT 9
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