Tools for Post-Processing Predicted Values.

probably

Introduction

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.

Installation

You can install probably from CRAN with:

install.packages("probably")

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

# install.packages("pak")
pak::pak("tidymodels/probably")

Examples

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.

Contributing

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.