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

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

* 23 pages, 6 tables, 3 figures 

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Learning Multiple Defaults for Machine Learning Algorithms

Nov 23, 2018
Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas Müller, Bernd Bischl


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Tunability: Importance of Hyperparameters of Machine Learning Algorithms

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

* 22 pages, 10 tables, 8 figures 

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Automatic Exploration of Machine Learning Experiments on OpenML

Oct 19, 2018
Daniel Kühn, Philipp Probst, Janek Thomas, Bernd Bischl

* 6 pages, 0 figures 

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Hyperparameters and Tuning Strategies for Random Forest

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

* 18 pages, 2 figures 

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

May 16, 2017
Philipp Probst, Anne-Laure Boulesteix

* 20 pages, 4 figures 

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Multilabel Classification with R Package mlr

Apr 03, 2017
Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl, Bernd Bischl

* The R Journal 9/1 (2017) 352-369 
* 18 pages, 2 figures, to be published in R Journal; reference corrected 

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mlr Tutorial

Sep 18, 2016
Julia Schiffner, Bernd Bischl, Michel Lang, Jakob Richter, Zachary M. Jones, Philipp Probst, Florian Pfisterer, Mason Gallo, Dominik Kirchhoff, Tobias Kühn, Janek Thomas, Lars Kotthoff


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