MaartenGr / MaartenGr/PolyFuzz
Analyse precision recall curve
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- Dominant language
- Python
- Stars
- 803
- Forks
- 72
- PR merge metrics
- No merged PRs in 30d
Description
I have two questions:
1. The precision-recall curve is a trade off between the min similarity and the percentage matched. So in the ideal case you want both the precision as the recall as high as possible. However I found out in my results that the model with the highest precision and recall isn't always the best. Am I missing something?
2. How would I set the optimal threshold for the similarity? Is this also based on the precision recall curve?
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First steps
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Research direction
The issue mentions precision-recall curves, similarity thresholds, and model results but names no file, test, or entry point. Start by reviewing the project's evaluation and similarity-threshold documentation, if present, and clarify the intended guidance before making a documentation change.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100