Evaluate AUC on a subset of items
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 5.1k
- Forks
- 724
- PR merge metrics
- No merged PRs in 30d
Description
Hi everyone,
As from the title, I need to evaluate AUC for categories of items, in other words, I need to understand for a category (i.e. meat) if items with positive interactions score generally higher than negative ones. However, built-in auc metric requires the whole test set at once.
Is there a handy way to achieve what I need?
Contributor guide
No contributing guide indexed for this repository
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 built-in AUC metric and how it consumes the whole test set. Clarify how item categories would be supplied and how per-category results should be defined, then identify the relevant metric tests or entry points. Done should include a clear, tested way to evaluate AUC for a selected item category.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100