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Current version of the SuperLearner R package
SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.
D-Lab's Machine Learning Working Group at UC Berkeley
Variable importance through targeted causal inference, with Alan Hubbard
4-hour tutorial on machine learning in R: knn, decision trees, random forest, boosting, superlearner
Multicore and multi-node parallel R computation via SLURM on the Savio cluster at UC Berkeley, plus XSEDE
1,190 contributions in the last year
Do you know if there's a way to parallelize the OOBCurve analysis? I am analyzing a large dataset and see that it's using only one core, but…
in private repositories
Sep 1 – Sep 24
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