Traditional model evaluation metrics fail to capture model performance under less than ideal conditions. This package employs techniques to evaluate models "under-stress". This includes testing models' extrapolation ability, or testing accuracy on specific sub-samples of the overall model space. Details describing stress-testing methods in this package are provided in Haycock (2023) <doi:10.26076/2am5-9f67>. The other primary contribution of this package is provided to R users access to the 'Python' library 'PyCaret' <https://pycaret.org/> for quick and easy access to auto-tuned machine learning models.
Version: 0.2.0 Depends: R (≥ 3.5) Imports: reticulate, stats, dplyr Suggests: knitr, rmarkdown, ggplot2, mlbench, testthat (≥ 3.0.0) Published: 2024-05-01 DOI: 10.32614/CRAN.package.stressor Author: Sam Haycock [aut, cre], Brennan Bean [aut], Utah State University [cph, fnd], Thermo Fisher Scientific Inc. [fnd] Maintainer: Sam Haycock <haycock.sam at outlook.com> License: MIT + file LICENSE NeedsCompilation: no SystemRequirements: python(>=3.8.10) Materials: README CRAN checks: stressor results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=stressor to link to this page.
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