JuliaAI / JuliaAI/DataScienceTutorials.jl
More sophisticated example of learning networks
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Description
@ablaom it would be nice to have a quick example of something that can't be done with a pipeline; I was thinking something like
L1 : standardizer and boxcox
L2 : Ridge and DTR
L3 : hcat and feed to a LinearRegressor
It should look something like this
W = X |> Standardizer()
z = y |> UnivariateBoxCoxTransformer()
ẑ₁ = (W, z) |> RidgeRegressor()
ẑ₂ = (W, z) |> DecisionTreeRegressor()
R = hcat(ẑ₁, ẑ₂)
ẑ = (R, z) |> LinearRegressor()
ŷ = ẑ |> inverse_transform(z)
but it looks like there's an issue with fitting the R node, could you comment on it?
ERROR: MethodError: no method matching ridge(::Array{Any,2}, ::Array{Float64,1}, ::Float64)
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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
Start with the Julia learning-network example in the issue, including Standardizer, UnivariateBoxCoxTransformer, RidgeRegressor, DecisionTreeRegressor, hcat, and LinearRegressor. Reproduce the reported error at the R node and determine what prevents fitting it. Done means the more sophisticated example is documented and runs without the shown MethodError.
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Assessment
- Tech stack
- julia
- Domain
- documentation, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100