How can I parallelize fitting a gradient boosting model with sklearn?
There are alternatives like XGboost or LigtGMB that are distributed (i.e., can run parallel). These are well documented and popular boosting algorithms.
Relatively new to model building with sklearn. I know cross validation can be parallelized via the n_jobs parameter, but if I'm not using CV, how can I utilize my available cores to speed up model fitting?