JuliaAI / JuliaAI/MLJLinearModels.jl
API to construct MLJ-proper model from standalone model
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- Dominant language
- Julia
- Stars
- 86
- Forks
- 15
- PR merge metrics
- No merged PRs in 30d
Description
julia> enet = ElasticNetRegression()
GeneralizedLinearRegression{L2Loss, CompositePenalty}
loss: L2Loss L2Loss()
penalty: CompositePenalty
fit_intercept: Bool true
penalize_intercept: Bool false
scale_penalty_with_samples: Bool true
julia> ElasticNetRegressor(enet)
ERROR: MethodError: no method matching ElasticNetRegressor(::GeneralizedLinearRegression{L2Loss, CompositePenalty})
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 tracing the definitions and constructors for ElasticNetRegression and ElasticNetRegressor, then inspect how standalone model instances are converted into MLJ-proper models. Done means ElasticNetRegressor(enet) accepts the ElasticNetRegression instance shown and preserves the expected model configuration.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 42/100