6 papers · 1 filter
Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications
Christina Sauer, Anne-Laure Boulesteix, Luzia Hanßum +3
Adequately generating and evaluating prediction models based on supervised machine learning (ML) is often challenging, especially for less experienced users in applied research are…
Constructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Study
Hannah Schulz-Kümpel, Sebastian Fischer, Roman Hornung +3
When assessing the quality of prediction models in machine learning, confidence intervals (CIs) for the generalization error, which measures predictive performance, are a crucial t…
Evaluating machine learning models in non-standard settings: An overview and new findings
Roman Hornung, Malte Nalenz, Lennart Schneider +5
Estimating the generalization error (GE) of machine learning models is fundamental, with resampling methods being the most common approach. However, in non-standard settings, parti…
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…
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…