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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 |
| 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 |
| Reference manual: | stressor.pdf |
| Vignettes: |
stressor |
| Package source: | stressor_0.2.0.tar.gz |
| Windows binaries: | r-devel: stressor_0.2.0.zip, r-release: stressor_0.2.0.zip, r-oldrel: stressor_0.2.0.zip |
| macOS binaries: | r-release (arm64): stressor_0.2.0.tgz, r-oldrel (arm64): stressor_0.2.0.tgz, r-release (x86_64): stressor_0.2.0.tgz, r-oldrel (x86_64): stressor_0.2.0.tgz |
| Old sources: | stressor archive |
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