49 citations · 70 across the 5 of their papers we have counts for
5 papers
Overcoming Barriers to Computational Reproducibility
Roman Hornung, László Németh, Oleksandr Zadorozhny +13
Computational reproducibility, the possibility for independent researchers to exactly reproduce published empirical results, is fundamental to science. Despite its importance, the…
Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications
Christina Sauer, Anne-Laure Boulesteix, Luzia Hanßum +3
Adequately generating and evaluating prediction models based on supervised machine learning (ML) is often challenging, especially for less experienced users in applied research are…
Explaining the optimistic performance evaluation of newly proposed methods: a cross-design validation experiment
Christina Nießl, Sabine Hoffmann, Theresa Ullmann +1
The constant development of new data analysis methods in many fields of research is accompanied by an increasing awareness that these new methods often perform better in their intr…
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…
Validation of cluster analysis results on validation data: A systematic framework
Theresa Ullmann, Christian Hennig, Anne-Laure Boulesteix
Cluster analysis refers to a wide range of data analytic techniques for class discovery and is popular in many application fields. To judge the quality of a clustering result, diff…