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

library(tidyrules)

model_c5 = C50::C5.0(Species ~ ., data = iris, rules = TRUE)
pander::pandoc.table(tidy(model_c5), split.tables = 120)
#> 
#> ----------------------------------------------------------------------------------------------
#>  rule_nbr   trial_nbr              LHS                  RHS       support   confidence   lift 
#> ---------- ----------- ---------------------------- ------------ --------- ------------ ------
#>     1           1        ( Petal.Length <= 1.9 )       setosa       50        0.9808     2.9  
#> 
#>     2           1       ( Petal.Length > 1.9 ) & (   versicolor     48         0.96      2.9  
#>                         Petal.Length <= 4.9 ) & (                                             
#>                            Petal.Width <= 1.7 )                                               
#> 
#>     3           1         ( Petal.Width > 1.7 )      virginica      46        0.9583     2.9  
#> 
#>     4           1         ( Petal.Length > 4.9 )     virginica      46        0.9375     2.8  
#> ----------------------------------------------------------------------------------------------

Installation

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")