125 citations · 164 across the 5 of their papers we have counts for
6 papers
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…
Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study
Roman Hornung, Frederik Ludwigs, Jonas Hagenberg +1
As the availability of omics data has increased in the last few years, more multi-omics data have been generated, that is, high-dimensional molecular data consisting of several typ…
Sequential Permutation Testing of Random Forest Variable Importance Measures
Alexander Hapfelmeier, Roman Hornung, Bernhard Haller
Hypothesis testing of random forest (RF) variable importance measures (VIMP) remains the subject of ongoing research. Among recent developments, heuristic approaches to parametric…
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…
A U-statistic estimator for the variance of resampling-based error estimators
Mathias Fuchs, Roman Hornung, Riccardo De Bin +1
We revisit resampling procedures for error estimation in binary classification in terms of U-statistics. In particular, we exploit the fact that the error rate estimator involving…