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Anne-Laure Boulesteix

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Position Paper: Rethinking Empirical Research in Machine Learning: Addressing Epistemic and Methodological Challenges of Experimentation

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May 03, 2024
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl

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Understanding random forests and overfitting: a visualization and simulation study

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Feb 28, 2024
Lasai Barreñada, Paula Dhiman, Dirk Timmerman, Anne-Laure Boulesteix, Ben Van Calster

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Evaluating machine learning models in non-standard settings: An overview and new findings

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Oct 23, 2023
Roman Hornung, Malte Nalenz, Lennart Schneider, Andreas Bender, Ludwig Bothmann, Bernd Bischl, Thomas Augustin, Anne-Laure Boulesteix

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Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study

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Feb 08, 2023
Roman Hornung, Frederik Ludwigs, Jonas Hagenberg, Anne-Laure Boulesteix

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

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Jul 14, 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

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Large-scale benchmark study of survival prediction methods using multi-omics data

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Mar 07, 2020
Moritz Herrmann, Philipp Probst, Roman Hornung, Vindi Jurinovic, Anne-Laure Boulesteix

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Tunability: Importance of Hyperparameters of Machine Learning Algorithms

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Oct 22, 2018
Philipp Probst, Bernd Bischl, Anne-Laure Boulesteix

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Hyperparameters and Tuning Strategies for Random Forest

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Apr 10, 2018
Philipp Probst, Marvin Wright, Anne-Laure Boulesteix

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To tune or not to tune the number of trees in random forest?

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May 16, 2017
Philipp Probst, Anne-Laure Boulesteix

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A U-statistic estimator for the variance of resampling-based error estimators

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Dec 18, 2013
Mathias Fuchs, Roman Hornung, Riccardo De Bin, Anne-Laure Boulesteix

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