Standardizer silently ignores features with missing values.
Nobody has claimed this yet.
Assessment
- Difficulty
- 3/5
- Estimated time
- Half a day
- Newbie friendliness
- 42/100
- Issue type
- Bug
- Clarity
- Mostly clear
- Activity status
- Active
- Domain
- machine-learning
Research direction
The issue says Standardizer drops features that contain missing values instead of passing them through. Start with the proposed changes in pull request #86, which the issue links, and find where the standardizer's fit and transform handle missing entries. Done means missing values are kept in the output and existing tests still pass. Since PR #86 already exists, check its status with the maintainers before starting.
Written by the indexing model from the issue text.
Description
I think we could safely propagate missing values. See this PR for proposed changes.
- Dominant language
- Julia
- Stars
- 1
- Forks
- 2
- Avg merge
- 9d 22h
- Merged PRs (30d)
- 2
Getting set up
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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.
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