JuliaAI / JuliaAI/MLJLinearModels.jl
Use fghv! in Hv! for MultinomialLoss
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Nobody has claimed this yet.
performances
- Dominant language
- Julia
- Stars
- 86
- Forks
- 15
- PR merge metrics
- No merged PRs in 30d
Description
use https://github.com/JuliaNLSolvers/Optim.jl/pull/742 in d_logistic.jl Hv!
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 d_logistic.jl and the referenced Optim.jl pull request 742, focusing on how fghv! is used for Hv! with MultinomialLoss. Done means the MultinomialLoss Hv! implementation uses the referenced fghv! approach consistently.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Refactor
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100