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I'm trying the R interpret package, the sample size is 100k with 100 features.
But I found that the training process is very slow and the function ebm_classify has no n_jobs argument.
The text was updated successfully, but these errors were encountered:
The R package is still very new and there's a lot more work we need to do in order to give it the same level of features as the more established python package. As you point out, training isn't parallelized currently in the R package, so it will be significantly slower on machines with many cores. Our plan is to move all the parallelism into the lower level C++ components where it can be shared across both python and R. We don't have a timeline yet for doing that work, but it will be at least several months. Once we have further news to share, we'll post it here in this issue.
I'm trying the R interpret package, the sample size is 100k with 100 features.
But I found that the training process is very slow and the function
ebm_classify
has non_jobs
argument.The text was updated successfully, but these errors were encountered: