Philipp Probst
Philipp Probst
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Title
Cited by
Cited by
Year
Hyperparameters and tuning strategies for random forest
P Probst, MN Wright, AL Boulesteix
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1301, 2018
752018
Random forest versus logistic regression: a large-scale benchmark experiment
R Couronné, P Probst, AL Boulesteix
BMC bioinformatics 19 (1), 270, 2018
702018
To Tune or Not to Tune the Number of Trees in Random Forest.
P Probst, AL Boulesteix
Journal of Machine Learning Research 18 (181), 1-18, 2018
652018
Tunability: importance of hyperparameters of machine learning algorithms
P Probst, B Bischl, AL Boulesteix
Journal of Machine Learning Research 20 (53), 1-32, 2018
592018
Ranger: A fast implementation of random forests
MN Wright, S Wager, P Probst
R package version 0.5. 0, 2016
272016
Multilabel classification with R package mlr
P Probst, Q Au, G Casalicchio, C Stachl, B Bischl
The R Journal 9 (1), 352--369, 2017
102017
tscount: Analysis of Count Time Series
T Liboschik, R Fried, K Fokianos, P Probst
R package version 1 (0), 2016
92016
Making complex prediction rules applicable for readers: Current practice in random forest literature and recommendations
AL Boulesteix, S Janitza, R Hornung, P Probst, H Busen, A Hapfelmeier
Biometrical Journal 61 (5), 1314-1328, 2019
52019
Automatic Exploration of Machine Learning Experiments on OpenML
D Kühn, P Probst, J Thomas, B Bischl
arXiv preprint arXiv:1806.10961, 2018
42018
Asthma features in severe COPD: Identifying treatable traits
S Matthes, J Stadler, J Barton, G Leuschner, D Munker, P Arnold, ...
Respiratory medicine 145, 89-94, 2018
32018
Learning Multiple Defaults for Machine Learning Algorithms
F Pfisterer, JN van Rijn, P Probst, A Müller, B Bischl
arXiv preprint arXiv:1811.09409, 2018
32018
mlr Tutorial
J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ...
arXiv preprint arXiv:1609.06146, 2016
32016
A fast implementation of random forests
MN Wright, S Wager, P Probst
R package version 0.11 2, 2019
22019
Package ‘ranger’
MN Wright, S Wager, P Probst, MMN Wright
22018
Hyperparameters, tuning and meta-learning for random forest and other machine learning algorithms
P Probst
lmu, 2019
12019
Analysis of Count Time Series
T Liboschik
1
Decompressive Craniectomy Is Associated With Good Quality of Life Up to 10 Years After Rehabilitation From Traumatic Brain Injury
K Rauen, L Reichelt, P Probst, B Schäpers, F Müller, K Jahn, N Plesnila
Critical Care Medicine, 2020
2020
Large-scale benchmark study of survival prediction methods using multi-omics data
M Herrmann, P Probst, R Hornung, V Jurinovic, AL Boulesteix
arXiv preprint arXiv:2003.03621, 2020
2020
Package ‘tscount’
T Liboschik, R Fried, K Fokianos, P Probst, J Rathjens, MT Liboschik
2020
Long-term follow-up after traumatic brain injury: what matters for good quality of life? A cross-sectional analysis up to 10 years after the brain injury: CROCFLAME
K Rauen, L Reichelt, P Probst, B Schaepers, F Mueller, K Jahn, N Plesnila
EUROPEAN JOURNAL OF NEUROLOGY 26, 185-185, 2019
2019
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Articles 1–20