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stat.ML2025
Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications
Christina Sauer, Anne-Laure Boulesteix, Luzia HanÃum +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…
stat.ML2025
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…