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.
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")
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 |
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.