“Existing hyperparameter optimization methods are highly parallel but make no effort to balance the search across heterogeneous hardware or to prioritize searching high-impact spaces,” says a team ...

Conclusions: In this case study, hyperparameter tuning produced a super learner that performed slightly better than an untuned super learner. Tuning the hyperparameters of individual algorithms in a ...

Well, within the Python ecosystem, the most widely used libraries are going to be Pandas, Scikit-learn, and XGBoost. The first change would be to add a scaling framework such as Dask to the solution.