MLJ w/Scikitlearn: passing return_std to predict

Specific Domains Machine Learning
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You’re not missing something, there’s currently no way to pass that argument. It might be good to open an issue at MLJScikitLearnInterface to discuss this (and you could paste what follows).

I doubt that MLJ’s predict signature will be adapted to match this one but I’ll let @ablaom or @samuel_okon discuss that).

What could work is to pass the return_std as a new field of BayesianRidgeRegressor here MLJScikitLearnInterface.jl/linear-regressors.jl at 36882f14321e7e9889aac31447eeed0102eb052f · JuliaAI/MLJScikitLearnInterface.jl · GitHub

then pick that up at predict time here MLJScikitLearnInterface.jl/macros.jl at 36882f14321e7e9889aac31447eeed0102eb052f · JuliaAI/MLJScikitLearnInterface.jl · GitHub

this would also require ScikitLearn.jl to allow passing a return_std=true to predict, that might also require opening an issue there cc @cstjean

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