stressor: Algorithms for Testing Models under Stress
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: |
0.1.0 |
| Depends: |
R (≥ 3.5) |
| Imports: |
reticulate, stats, dplyr |
| Suggests: |
knitr, rmarkdown, ggplot2, mlbench, testthat (≥ 3.0.0) |
| Published: |
2024-01-31 |
| 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:
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