Just a quick question about the gradient boosting in scikit-learn. We have
tons of data to regress on (like 100M data points), but the running time of
the algorithm is linear in the size of X no matter what subsample is set
to. Right now we just sample say 100k data points and run gradient boosting
on it, but it would be nice if we can use a much larger data set.
See
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/gradient_boosting.py#L587for
the code – basically instead of subsampling, the algorithm just
creates
a random binary mask.
It would be nice if it was linear in the len(X) * subsample because then we
could set subsample to a very small number and use a lot more data points.
That should reduce overfitting with no disadvantages really (afaik). I'm
new to gradient boosting and I don't know it that well. Is there a
fundamental reason why you can't make it linear in len(X) * subsample?
Otherwise I might try to put together a patch for it.
Thanks!
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