JuliaAI / JuliaAI/MLJLinearModels.jl
LOOCV / GCV for ridge / logistic
Open
Nobody has claimed this yet.
API
enhancement
high priority
- Dominant language
- Julia
- Stars
- 86
- Forks
- 15
- PR merge metrics
- No merged PRs in 30d
Description
Ridge
refs
- https://arxiv.org/pdf/1509.09169.pdf
- GCV and GCVPack (probably a good place to start): http://pages.stat.wisc.edu/~wahba/stat860public/pdf1/775r.pdf
- page 22 https://elib.uni-stuttgart.de/bitstream/11682/9236/1/BA_fertig_QianKun_25072017.pdf
- slides https://www.wias-berlin.de/workshops/validation2010/workshop_slides/talk_goeman.pdf
Basically just have to minimise a criterion that can be evaluated in constant time after the first fit.
Similar stuff can be obtained for LOOCV it seems
proper refs
Logistic: https://arxiv.org/pdf/1711.05420.pdf
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 reading the linked references on GCV and LOOCV for ridge regression, then review the repository's existing ridge and logistic regression implementations. The issue does not name files or tests. Done would mean adding the requested criteria for ridge and logistic models, with evaluation after the initial fit as described.
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
- 20/100