1.6k citations · 2.5k across the 7 of their papers we have counts for
4 papers · 1 filter
Learning Multiple Defaults for Machine Learning Algorithms
Florian Pfisterer, Jan N. van Rijn, Philipp Probst +2
The performance of modern machine learning methods highly depends on their hyperparameter configurations. One simple way of selecting a configuration is to use default settings, of…
Automatic Exploration of Machine Learning Experiments on OpenML
Daniel Kühn, Philipp Probst, Janek Thomas +1
Understanding the influence of hyperparameters on the performance of a machine learning algorithm is an important scientific topic in itself and can help to improve automatic hyper…
Hyperparameters and Tuning Strategies for Random Forest
Philipp Probst, Marvin Wright, Anne-Laure Boulesteix
The random forest algorithm (RF) has several hyperparameters that have to be set by the user, e.g., the number of observations drawn randomly for each tree and whether they are dra…
Tunability: Importance of Hyperparameters of Machine Learning Algorithms
Philipp Probst, Bernd Bischl, Anne-Laure Boulesteix
Modern supervised machine learning algorithms involve hyperparameters that have to be set before running them. Options for setting hyperparameters are default values from the softw…