BayBiggies March 2021 Online Meeting: How to Reduce Scikit-Learn Training Time

Why watch

This tutorial covers three main approaches to reducing scikit-learn training time: selecting appropriate solvers (such as switching to LBFGS, SAG, or SAGA), optimizing hyperparameters through grid search, random search, or early stopping, and parallelizing training using joblib or Ray distributed computing. Viewers will learn the trade-offs between each method, including when to use batch optimization versus iterative solvers, and how to evaluate which approach provides the best speedup for their specific use case.