2 papers
cs.LG2023
Comparing Machine Learning Algorithms by Union-Free Generic Depth
Hannah Blocher, Georg Schollmeyer, Malte Nalenz +1
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies in linear and metric spaces, there is v…
stat.ML2023
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…