Philipp Probst - researchr alias

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Viewing Publication 1 - 9 from 9

2021

Large-scale benchmark study of survival prediction methods using multi-omics dataMoritz Herrmann, Philipp Probst, Roman Hornung, Vindi Jurinovic, Anne-Laure Boulesteix. bib, 22(3), 2021. [doi]

Learning multiple defaults for machine learning algorithmsFlorian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas C. Müller, Bernd Bischl. gecco 2021: 241-242 [doi]

2019

Hyperparameters, tuning and meta-learning for random forest and other machine learning algorithmsPhilipp Probst. PhD thesis, Ludwig Maximilian University of Munich, Germany, 2019. [doi]

Hyperparameters and tuning strategies for random forestPhilipp Probst, Marvin N. Wright, Anne-Laure Boulesteix. widm, 9(3), 2019. [doi]

Tunability: Importance of Hyperparameters of Machine Learning AlgorithmsPhilipp Probst, Anne-Laure Boulesteix, Bernd Bischl. jmlr, 20, 2019. [doi]

2018

Random forest versus logistic regression: a large-scale benchmark experimentRaphael Couronné, Philipp Probst, Anne-Laure Boulesteix. bmcbi, 19(1), 2018. [doi]

2017

To Tune or Not to Tune the Number of Trees in Random ForestPhilipp Probst, Anne-Laure Boulesteix. jmlr, 18, 2017. [doi]

Multilabel Classification with R Package mlrPhilipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl, Bernd Bischl. rjour, 9(1):352, 2017. [doi]

2012

Object circles: modeling physical objects as social relationshipsFlorian Michahelles, Philipp Probst. mum 2012: 23 [doi]

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