49 citations · 49 across the 6 of their papers we have counts for
3 papers · 1 filter
mlr3mbo: Bayesian Optimization in R
Marc Becker, Lennart Schneider, Martin Binder +2
We present mlr3mbo, a modular toolbox for Bayesian optimization in R. mlr3mbo supports single- and multi-objective optimization, multi-point proposals, batch and asynchronous paral…
mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R
Patrick Schratz, Marc Becker, Michel Lang +1
Spatial and spatiotemporal machine-learning models require a suitable framework for their model assessment, model selection, and hyperparameter tuning, in order to avoid error esti…
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
Bernd Bischl, Martin Binder, Michel Lang +9
Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance. To avoid a time consuming…