JuliaAI / JuliaAI/MLJLinearModels.jl
box (or just positive) constraints on enet OLS
Open
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 86
- Forks
- 15
- PR merge metrics
- No merged PRs in 30d
Description
what would it take to support box / positive constraints on the Lasso / ElasticNet solvers? is this compatible with the existing API, and if so where could I get started on contributing to the implementation?
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 by reviewing the existing API and the Lasso/ElasticNet solver entry points to determine how constraints could be represented. Done means establishing API compatibility and implementing box or positive constraints for the relevant solvers, with coverage demonstrating the constrained behavior.
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