Predicting using MLJ interface is slow

[pat-alt]

@pasq-cat I think we should indeed just go with the direct MLJ interface #121 but for now I would still merge this cause it addresses the compute times.

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Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 96.48%. Comparing base (78c846e) to head (9873c55). Report is 5 commits behind head on main.

Additional details and impacted files

```diff @@ Coverage Diff @@ ## main #123 +/- ## ========================================== - Coverage 96.65% 96.48% -0.17% ========================================== Files 22 22 Lines 658 655 -3 ========================================== - Hits 636 632 -4 - Misses 22 23 +1 ```

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[pat-alt]

shoudln't the same be done in function MLJFlux.predict(model::LaplaceRegression, fitresult, Xnew)

?

Yes, I've done that

[pasq-cat]

shoudln't the same be done in function MLJFlux.predict(model::LaplaceRegression, fitresult, Xnew) ?

Yes, I've done that

btw i was sure that laplace accepted vectors as input. has anything been changed or i was wrong the whole time? the documentation says abstractarray but i distinctly remember this requirement....

[pat-alt]

shoudln't the same be done in function MLJFlux.predict(model::LaplaceRegression, fitresult, Xnew) ?

Yes, I've done that

btw i was sure that laplace accepted vectors as input. has anything been changed or i was wrong the whole time? the documentation says abstractarray but i distinctly remember this requirement....

hmm not sure where you say this https://juliatrustworthyai.github.io/LaplaceRedux.jl/stable/reference/#LaplaceRedux.predict-Tuple{LaplaceRedux.AbstractLaplace,%20AbstractArray}

[pasq-cat]

shoudln't the same be done in function MLJFlux.predict(model::LaplaceRegression, fitresult, Xnew) ?

Yes, I've done that

btw i was sure that laplace accepted vectors as input. has anything been changed or i was wrong the whole time? the documentation says abstractarray but i distinctly remember this requirement....

hmm not sure where you say this https://juliatrustworthyai.github.io/LaplaceRedux.jl/stable/reference/#LaplaceRedux.predict-Tuple{LaplaceRedux.AbstractLaplace,%20AbstractArray}

i know i know, must have been something that i have misunderstood in the first days, mah