Converting ranks into probability to calculate average precision
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- Dominant language
- Python
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Description
Hi!
Thank you for creating this very useful package. I'm working on implementing recommendation systems and I was trying to get the metric "average_recall/precision" from this package instead of just precision/recall@k. I noticed that the package itself generate rankings which is awesome , but is there a way to transform this back to probability to calculate the needed average precision?. I tried going through the source code but did not find it. Would be really appreciative if someone could point me in the right direction. Thanks!
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the package's ranking output and its existing precision/recall@k metrics. Determine whether ranked results can support probability conversion for average precision or average recall, and define the expected metric behavior before proposing a change. Done should include a clear implementation or documentation of the supported approach.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100