Building a classifier on changeset comments
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 19
- Forks
- 6
- PR merge metrics
- No merged PRs in 30d
Description
Changeset comments can be super interesting! Can a model be trained to learn what changeset comments of 👍 changesets and 👎 changesets look like?
- https://osmcha.mapbox.com/47469915/
> minor edits / repetition / redundancy / abreviation / duplication / contraction / shortening / cleaning up overbloating /
- https://osmcha.mapbox.com/47625854/
> Beautiful Fountain nice place for tourist. and a nice grass park where you could sit down and enjoy nature in the city
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cc: @anandthakker @geohacker @batpad
Contributor guide
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
No file, test, or entry point is named. Start by reviewing the two linked OSMCha changesets and the repository's existing Jupyter notebooks, then determine how 👍 and 👎 changesets are represented and what evaluation would demonstrate a useful classifier.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, scikit-learn
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100