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

TL;DR

fastml is an R package that enhances machine learning safety by preventing data leakage through guarded resampling, supporting various data scenarios and providing reliable performance estimates.

Contribution

It introduces guarded resampling in R, ensuring preprocessing is re-estimated within each fold to prevent leakage and improve model evaluation reliability.

Findings

Global preprocessing inflates performance estimates compared to guarded resampling.

fastml matches tidymodels performance while simplifying workflow.

Supported consistent benchmarking of survival models across datasets.

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, and it supported consistent benchmarking of multiple survival model classes through a unified interface.