Probst

Articles

• F. Pfisterer, J. N. van Rijn, P. Probst, A. Müller, and B. Bischl. Learning multiple defaults for machine learning algorithms. arXiv preprint arXiv:1811.09409, 2018. URL https://arxiv.org/abs/1811.09409 .
• P. Probst, B. Bischl, and A.-L. Boulesteix. Tunability: Importance of hyperparameters of machine learning algorithms. ArXiv preprint arXiv:1802.09596, 2018a. URL https://arxiv.org/abs/1802.09596 . 
• D. Kühn, P. Probst, J. Thomas, and B. Bischl. Automatic exploration of machine learning experiments on OpenML. ArXiv preprint arXiv:1806.10961, 2018. URL https://arxiv.org/abs/1806.10961  .
• P. Probst, M. N. Wright, and A.-L. Boulesteix. Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018b. doi: 10.1002/widm.1301. URL https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1301
• P. Probst and A.-L. Boulesteix. To tune or not to tune the number of trees in random forest. Journal of Machine Learning Research, 18(181):1–18, 2018. URL http://jmlr.org/papers/v18/17-269.html .
• R. Couronné, P. Probst, and A.-L. Boulesteix. Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinformatics, 19(1):270, 2018. URL https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2264-5
• P. Probst, Q. Au, G. Casalicchio, C. Stachl, and B. Bischl. Multilabel classification with R package mlr. The R Journal, 9(1):352–369, 2017. URL https://journal.r-project.org/archive/2017/RJ-2017-012/RJ-2017-012.pdf
• A.-L. Boulesteix, S. Janitza, R. Hornung, P. Probst, H. Busen, and A. Hapfelmeier. Making complex prediction rules applicable for readers: Current practice in random forest literature and recommendations. Biometrical Journal, 2016. URL https://www.ncbi.nlm.nih.gov/pubmed/30069934
• J. Schiffner, B. Bischl, M. Lang, J. Richter, Z.M. Jones, P.Probst, F. Pfisterer, M.Gallo, D.Kirchhoff and T. K{\"u}hn, J. Thomas. mlr Tutorial. arXiv preprint arXiv:1609.06146, 2016. URL https://arxiv.org/abs/1609.06146 

Software

• P. Probst. tuneRanger: Tune random forest of the ’ranger’ package, 2018a. URL https://CRAN.R-project.org/package=tuneRanger . R package version 0.4.
• P. Probst. measures: Performance Measures for Statistical Learning, 2018b. URL https://CRAN.R-project.org/package=measures . R package version 0.2.
• P. Probst. varImp: RF Variable Importance for Arbitrary Measures, 2018c. URL https://CRAN.R-project.org/package=varImp . R package version 0.2.
• T. Liboschik, R. Fried, K. Fokianos, P. Probst. tscount: Analysis of Count Time Series, 2016. URL https://CRAN.R-project.org/package=tscount . R package version 1.0.