JuliaAI / JuliaAI/MLJLinearModels.jl
Documentation
Open
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long-term
- Dominant language
- Julia
- Stars
- 86
- Forks
- 15
- PR merge metrics
- No merged PRs in 30d
Description
(see branch docs)
- the fact that, when we can, we avoid doing
hcat(X, 1) - use
IterativeSolvers.cgand notIterativeSolvers.lsqras it allows specifying the operator as a linear map which is efficient and avoids copying when having to add a column for X; anyway it should be identical apart from pathological cases - robust regression with concomitant scale estimation, not done yet
- LAD/quantile explain weight clipping for pathological zero residual
- generally detail how algos do IWLS etc so that it's 100% clear what is being done
- Newton
- NewtonCG (explain briefly then link to Optim)
- LBFGS (explain briefly then link to Optim)
- IWLSCG
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
The issue refers only to a “docs” branch and names no files, tests, or entry points; start by locating that branch and its documentation structure. Done means completing the checklist: document solver choices, robust regression limitations, LAD/quantile weight clipping, and the Newton, NewtonCG, LBFGS, and IWLSCG algorithms.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100