Philipp Probst
Philipp Probst
Unknown affiliation
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Cited by
Cited by
Hyperparameters and tuning strategies for random forest
P Probst, MN Wright, AL Boulesteix
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1301, 2018
Tunability: importance of hyperparameters of machine learning algorithms
P Probst, B Bischl, AL Boulesteix
Journal of Machine Learning Research 20 (53), 1-32, 2018
Random forest versus logistic regression: a large-scale benchmark experiment
R Couronné, P Probst, AL Boulesteix
BMC bioinformatics 19, 1-14, 2018
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
Ranger: A fast implementation of random forests
MN Wright, S Wager, P Probst
R package version 0.12 1, 2020
Large-scale benchmark study of survival prediction methods using multi-omics data
M Herrmann, P Probst, R Hornung, V Jurinovic, AL Boulesteix
Briefings in bioinformatics 22 (3), bbaa167, 2021
Quality of life up to 10 years after traumatic brain injury: a cross-sectional analysis
K Rauen, L Reichelt, P Probst, B Schäpers, F Müller, K Jahn, N Plesnila
Health and quality of life outcomes 18, 1-12, 2020
Learning multiple defaults for machine learning algorithms
F Pfisterer, JN van Rijn, P Probst, AC Müller, B Bischl
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021
Multilabel classification with R package mlr
P Probst, Q Au, G Casalicchio, C Stachl, B Bischl
The R Journal 9 (1), 352--369, 2017
ranger: A Fast Implementation of Random Forests (2015)
MN Wright, S Wager, P Probst
R package, 2016
Quality of life after traumatic brain injury: a cross-sectional analysis uncovers age-and sex-related differences over the adult life span
K Rauen, CB Späni, MC Tartaglia, MT Ferretti, L Reichelt, P Probst, ...
GeroScience 43, 263-278, 2021
Package ‘ranger’
MN Wright, S Wager, P Probst, MMN Wright
Version 0.11 2, 2019
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 48 (8), 1157-1164, 2020
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
Automatic exploration of machine learning experiments on openml
D Kühn, P Probst, J Thomas, B Bischl
arXiv preprint arXiv:1806.10961, 2018
tscount: Analysis of Count Time Series
T Liboschik, R Fried, K Fokianos, P Probst
R package version 1 (0), 2016
Hyperparameters, tuning and meta-learning for random forest and other machine learning algorithms
P Probst
lmu, 2019
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
Impact of pathologic body composition assessed by CT-based anthropometric measurements in adult patients with multiple trauma: A retrospective analysis
B Poros, T Irlbeck, P Probst, A Volkmann, P Paprottka, W Böcker, ...
European Journal of Trauma and Emergency Surgery 47, 1089-1103, 2021
Modern total knee arthroplasty (TKA): with or without a tourniquet?
S Nicolaiciuc, P Probst, R von Eisenhart-Rothe, R Burgkart, R Hube
Surgical Technology International 35, 336-340, 2019
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