In talegari/tidyrules: Utilities to Retrieve Rulelists from Model Fits, Filter, Prune, Reorder and Predict on Unseen Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
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)
devtools::load_all() #todo

tidyrules

tidyrules R package provides a framework to work with decision rules. Rules can be extracted from supported models, augmented with (custom) metrics using validation data, manipulated using standard dataframe operations, reordered and pruned based on a metric, predict on unseen (test) data. Utilities include; Creating a rulelist manually, Exporting a rulelist as a SQL case statement and so on. The package offers two classes; rulelist and ruleset based on dataframe.

website: https://talegari.github.io/tidyrules/

Example

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library(tidyrules)

model_c5 = C50::C5.0(Species ~ ., data = iris, rules = TRUE)
tidy(model_c5)

Installation

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

install.packages("tidyrules")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("talegari/tidyrules")

talegari/tidyrules documentation built on Feb. 7, 2025, 8:47 p.m.