README.md

In rules: Model Wrappers for Rule-Based Models

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.

| model | engine | mode | |:-------------|:-------|:---------------| | 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.

rules documentation built on March 31, 2023, 8:24 p.m.