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probably contains tools to facilitate activities such as:
Conversion of probabilities to discrete class predictions.
Investigating and estimating optimal probability thresholds.
Calibration assessments and remediation for classification and regression models.
Inclusion of equivocal zones where the probabilities are too uncertain to report a prediction.
You can install probably from CRAN with:
You can install the development version of probably from GitHub with:
Good places to look for examples of using probably are the vignettes.
vignette("equivocal-zones", "probably") discusses the new class_pred class that probably provides for working with equivocal zones.
vignette("where-to-use", "probably") discusses how probably fits in with the rest of the tidymodels ecosystem, and provides an example of optimizing class probability thresholds.
This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.
If you think you have encountered a bug, please submit an issue.
Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.
Check out further details on contributing guidelines for tidymodels packages and how to get help.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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