JuliaAI / JuliaAI/MLJScikitLearnInterface.jl
Some `Deterministic` models could actually be re-implemented as `Probabilistic`
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 14
- Forks
- 8
- PR merge metrics
- No merged PRs in 30d
Description
A case in point is BayesianRidgeRegressor, as pointed out at https://github.com/JuliaAI/MLJScikitLearnInterface.jl/issues/46.
Be good to first identify what other sk-learn models with MLJ implementations have the return_std predict kwarg. Help with this appreciated.
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 reviewing the MLJ implementations of scikit-learn models and checking which prediction interfaces expose the return_std keyword. Use BayesianRidgeRegressor and the linked issue as the initial comparison point; done means identifying the applicable models and determining which can be reimplemented as probabilistic models.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia, python, scikit-learn
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100