1.6k citations · 2.5k across the 7 of their papers we have counts for
8 papers
Large-scale benchmark study of survival prediction methods using multi-omics data
Moritz Herrmann, Philipp Probst, Roman Hornung +2
Multi-omics data, that is, datasets containing different types of high-dimensional molecular variables (often in addition to classical clinical variables), are increasingly generat…
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…
To tune or not to tune the number of trees in random forest?
Philipp Probst, Anne-Laure Boulesteix
The number of trees T in the random forest (RF) algorithm for supervised learning has to be set by the user. It is controversial whether T should simply be set to the largest compu…
Multilabel Classification with R Package mlr
Philipp Probst, Quay Au, Giuseppe Casalicchio +2
We implemented several multilabel classification algorithms in the machine learning package mlr. The implemented methods are binary relevance, classifier chains, nested stacking, d…
mlr Tutorial
Julia Schiffner, Bernd Bischl, Michel Lang +9
This document provides and in-depth introduction to the mlr framework for machine learning experiments in R.