[2604.05225] fastml: Guarded Resampling Workflows for Safer Automated Machine Learning in R

Statistics > Computation arXiv:2604.05225 (stat) [Submitted on 6 Apr 2026] Title:fastml: Guarded Resampling Workflows for Safer Automated Machine Learning in R Authors:Selcuk Korkmaz, Dincer Goksuluk, Eda Karaismailoglu View a PDF of the paper titled fastml: Guarded Resampling Workflows for Safer Automated Machine Learning in R, by Selcuk Korkmaz and 2 other authors View PDF HTML (experimental) Abstract:Preprocessing leakage arises when scaling, imputation, or other data-dependent transformations are estimated before resampling, inflating apparent performance while remaining hard to detect. We present fastml, an R package that provides a single-call interface for leakage-aware machine learning through guarded resampling, where preprocessing is re-estimated inside each resample and applied to the corresponding assessment data. The package supports grouped and time-ordered resampling, blocks high-risk configurations, audits recipes for external dependencies, and includes sandboxed execution and integrated model explanation. We evaluate fastml with a Monte Carlo simulation contrasting global and fold-local normalization, a usability comparison with tidymodels under matched specifications, and survival benchmarks across datasets of different sizes. The simulation demonstrates that global preprocessing substantially inflates apparent performance relative to guarded resampling. fastml matched held-out performance obtained with tidymodels while reducing workflow orchestration, an...