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) . 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.


stressor

R-CMD-check

Installation

You can install the development version of stressor from GitHub with:

# install.packages("devtools")
devtools::install_github("beanb2/stressor")

Reference manual

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install.packages("stressor")

0.2.0 by Sam Haycock, 2 years ago


Browse source code at https://github.com/cran/stressor


Authors: Sam Haycock [aut, cre] , Brennan Bean [aut] , Utah State University [cph, fnd] , Thermo Fisher Scientific Inc. [fnd]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports reticulate, stats, dplyr

Suggests knitr, rmarkdown, ggplot2, mlbench, testthat

System requirements: python(>=3.8.10)


See at CRAN