JuliaAI / JuliaAI/MLJTransforms.jl
Enhance treatment of missing value in one-hot encoder
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- Dominant language
- Julia
- Stars
- 1
- Forks
- 2
- PR merge metrics
- No merged PRs in 30d
Description
There is now missing value handling in OneHotEncoder but this simply propagates the missing values. I guess it might be nice to offer some other popular options for handling missing values which might be complicated to handle in a post-processing step. See also the discussion here.
@Chandu-4444 @Frank-III @OlivierLabayle
Contributor guide
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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 with the OneHotEncoder missing-value handling added in issue #457, then read the discussion linked from MLJModels.jl issue #450. Clarify which popular missing-value options should be supported and how they should behave; done means the agreed options are implemented and their behavior is verified.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100