JuliaAI / JuliaAI/MLJLinearModels.jl
Other models / solvers
Open
Nobody has claimed this yet.
low priority
- Dominant language
- Julia
- Stars
- 86
- Forks
- 15
- PR merge metrics
- No merged PRs in 30d
Description
(see readme)
- Ridge classification
- LARS see LARS.jl
- Quantile regression see [QuantileRegression.jl] (https://github.com/pkofod/QuantileRegression.jl) see also #4 and #5
- glmnet see GLMNet wrapper
- prox Newton or L-BFGS (see also https://github.com/yuekai/PNOPT; and paper https://arxiv.org/pdf/1206.1623.pdf as well as https://papers.nips.cc/paper/5384-proximal-quasi-newton-for-computationally-intensive-l1-regularized-m-estimators.pdf)
- count -- http://hua-zhou.github.io/media/pdf/ZhangZhouZhouSun17mglm.pdf
- Ridge classifier -- https://stackoverflow.com/questions/53911663/what-does-sklearn-ridgeclassifier-do/53912015
- TheilSen -- https://en.wikipedia.org/wiki/Theil–Sen_estimator
Other stuff
- add option for size of tape in LBFGS solver(s)
- add option(s) for linesearches
- add and test options generally for solvers
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 with the README referenced by the issue, then separate the proposed models from the solver enhancements. Review LARS.jl, QuantileRegression.jl, the GLMNet wrapper, and the linked prox-Newton references to determine scope. Done would require an agreed subset of models and solver options, with corresponding tests.
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