JuliaAI / JuliaAI/MLJLinearModels.jl
Add benchmarks
Open
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 86
- Forks
- 15
- PR merge metrics
- No merged PRs in 30d
Description
Against
- sklearn
- R
- quantilereg.jl
- glm.jl
- ...
in the benchmark use
-- speed to find the parameter (ratio to fastest)
-- objective function (ratio to best)
use default tol settings to make stuff easier to reproduce
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 locating the benchmark entry point in MLJLinearModels.jl and determine how the listed sklearn, R, quantilereg.jl, and glm.jl comparisons should be run. Done means reporting speed-to-fastest and objective-to-best ratios using default tolerance settings, with results reproducible across the named implementations.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia, python, r
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100