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rules

Introduction

rules is a parsnip extension package with model definitions for rule-based models, including:

cubist models that have discrete rule sets that contain linear models with an ensemble method similar to boosting
classification rules where a ruleset is derived from an initial tree fit
rule-fit models that begin with rules extracted from a tree ensemble which are then added to a regularized linear or logistic regression.

Installation

You can install the released version of rules from CRAN with:

install.packages("rules")

Install the development version from GitHub with:

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

Available Engines

The rules package provides engines for the models in the following table.

C5_rules C5.0 classification cubist_rules Cubist regression rule_fit xrf classification rule_fit xrf regression 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.

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