mlr3summary: Concise and interpretable summaries for machine learning models

1 code implementation • 25 Apr 2024 • Susanne Dandl, Marc Becker, Bernd Bischl, Giuseppe Casalicchio, Ludwig Bothmann

This work introduces a novel R package for concise, informative summaries of machine learning models.

Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers

1 code implementation • 29 Nov 2021 • Julia Moosbauer, Martin Binder, Lennart Schneider, Florian Pfisterer, Marc Becker, Michel Lang, Lars Kotthoff, Bernd Bischl

Automated hyperparameter optimization (HPO) has gained great popularity and is an important ingredient of most automated machine learning frameworks.

Bayesian Optimization Hyperparameter Optimization

mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R

1 code implementation • 25 Oct 2021 • Patrick Schratz, Marc Becker, Michel Lang, Alexander Brenning

This contribution reviews the state-of-the-art in spatial and spatiotemporal cross-validation, and introduces the {R} package {mlr3spatiotempcv} as an extension package of the machine-learning framework {mlr3}.

BIG-bench Machine Learning Model Selection

Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges

no code implementations • 13 Jul 2021 • Bernd Bischl, Martin Binder, Michel Lang, Tobias Pielok, Jakob Richter, Stefan Coors, Janek Thomas, Theresa Ullmann, Marc Becker, Anne-Laure Boulesteix, Difan Deng, Marius Lindauer

Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance.

Bayesian Optimization BIG-bench Machine Learning +2