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More support for tree-based regressors #3598

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smartmic opened this issue Jan 9, 2024 · 3 comments
Closed

More support for tree-based regressors #3598

smartmic opened this issue Jan 9, 2024 · 3 comments

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@smartmic
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smartmic commented Jan 9, 2024

Are there any plans to add more support for tree-based regression algorithms?
There is a DecisionTreeRegressor, but unfortunately not for AdaBoost or RandomForest.
I have used these for regression tasks in Scikit-Learn, and they have worked well for certain tasks.
It would be great if I could switch to mlpack here as well.

I have no idea what the effort would be, but I wonder: aren't the corresponding classifiers already half the rent?
A tangential discussion has been started in #3015 (comment)

@rcurtin
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rcurtin commented Jan 9, 2024

It would be nice to add both of those, but it's not completely trivial. For RandomForest I think you can pretty much adapt the existing code and implement a Predict() function; for AdaBoost, I think there may be some algorithmic things that need to change a little (but I don't remember the details). Anyway, if you or someone wanted to take the time to do it, I think it would be a nice contribution. Ideally we can reuse some of the code from RandomForest or AdaBoost, but, my memory is that there were some irritating details that meant that wasn't easy for DecisionTreeRegressor and DecisionTree.

@arthiondaena
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arthiondaena commented Feb 15, 2024

Hey @rcurtin, can I implement RandomForestRegressor and AdaBoostRegressor? Randomforest is pretty simple, and for AdaBoost there is some work to do.

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mlpack-bot bot commented Apr 3, 2024

This issue has been automatically marked as stale because it has not had any recent activity. It will be closed in 7 days if no further activity occurs. Thank you for your contributions! 👍

@mlpack-bot mlpack-bot bot added the s: stale label Apr 3, 2024
@mlpack-bot mlpack-bot bot closed this as completed Apr 10, 2024
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4 participants