fastml: Guarded Resampling Workflows for Safer Automated Machine Learning in R
This addresses data leakage issues for users of automated machine learning in R, offering a practical tool, but it is incremental as it builds on existing resampling methods.
The paper tackles preprocessing leakage in automated machine learning by introducing fastml, an R package that uses guarded resampling to re-estimate preprocessing inside each resample, preventing inflated performance; evaluation shows global preprocessing inflates apparent performance, and fastml matches held-out performance while reducing workflow complexity.
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, and it supported consistent benchmarking of multiple survival model classes through a unified interface.