JuliaAI / JuliaAI/MLJScikitLearnInterface.jl
Add sample weights support for sk-learn models
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- Dominant language
- Julia
- Stars
- 14
- Forks
- 8
- PR merge metrics
- No merged PRs in 30d
Description
Here is a list of sk-learn models that can take sample weights in training: https://github.com/alan-turing-institute/MLJ.jl/issues/327
The MLJ API supports sample weights; the convention is to overload fit(model, verbosity, X, y, w) where w is a vector of weights (with same length as y). Search for sample weights in the docs
Contributor guide
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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
Read the sample-weights guidance in the linked “Adding models for general use” documentation, then review the model list in MLJ.jl issue #327. Identify the listed ScikitLearn.jl models that support weights and verify that each follows the MLJ fit convention with a weight vector matching y.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia, scikit-learn
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100