Tools for Post-Processing Predicted Values

Models can be improved by post-processing class probabilities, by: recalibration, conversion to hard probabilities, assessment of equivocal zones, and other activities. 'probably' contains tools for conducting these operations as well as calibration tools and conformal inference techniques for regression models.

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.