JuliaAI / JuliaAI/DataScienceTutorials.jl
Multivariate Polynomial Regression
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- Dominant language
- ReScript
- Stars
- 126
- Forks
- 19
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Description
The tutorial is great, but here's where I'm at a loss: if I have multiple independent variables that I want to consider in the regression, how can I do that in a way that's similar to sklearns PolynomialFeatures: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html#sklearn.preprocessing.PolynomialFeatures
I've been using MLJLinearModels.jl where applicable, but would it be better to just sub-in the SKLearnMLJ interface?
If there's a way to do it that easily builds off of the tutorial and I've just missed that, pointing me in the right direction is also appreciated.
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First steps
- Read the whole issue, then the project's contributing guide.
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- Fork the repository and make your change on a branch.
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Research direction
Start by reading the existing regression tutorial and compare the MLJLinearModels and SKLearnMLJ approaches referenced in the issue. Determine whether the requested workflow belongs in the tutorial and what supported Julia path should be documented. Done means a maintainer-approved, reproducible multivariate polynomial regression example or a clear explanation of the supported approach.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia, scikit-learn
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100