Comments (5)
@EchoDel Thanks for raising this issue.
I would rather suggest to add an additional function that allows to return the trained lgb.Booster
so that you can use it for further analysis. What do you think?
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I'm looking for the parameters after the transformations and the probabilities so to avoid duplicating the code in the distribution class I would prefer either specifying the kwargs or being able to pass the raw predictions in to the distribution class to apply the transformations.
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Yeah, you are probably correct. Would be good to have the kwargs available. Not sure though when I find the time to look into it.
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So I can work through it and submit and PR which you can merge after the tests have been added but thinking about if there is a better way to do this.
Does it make sense to change the DistributionClass.predict_dist to take a predictions argument instead of the booster and test set, then moving the booster.predict into the LightGBMLSS.predict?
Again I can work through this and submit a PR if you are happy with this change.
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Sorry was busy with the recent releases. Yes makes sense, thank you. Happy if you create a first draft via a PR
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