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Decision trees, using C5.0

Source: vignettes/C5.0.Rmd
C5.0.Rmd
Function Works
tidypredict_fit(), tidypredict_sql(), parse_model()
tidypredict_to_column()
tidypredict_test()
parsnip

How it works

Here is a simple C5.0() classification model using the mtcars dataset:

library(dplyr)
library(tidypredict)
library(C50)

mtcars2 <- mtcars
mtcars2$vs <- factor(mtcars2$vs)

model <- C5.0(mtcars2[, c("wt", "cyl", "mpg")], mtcars2$vs)

Under the hood

C5.0 stores its fitted tree as text in the tree element of the model object. tidypredict parses that text directly, so the model can be translated even when it was fitted through the x/y interface (as parsnip does).

The tree is transformed into a dplyr, a.k.a Tidy Eval, formula. The decision tree becomes a nested dplyr::case_when() statement.

tidypredict_fit(model)
#> case_when(cyl <= 6 ~ case_when(cyl <= 4 ~ "1", .default = case_when(wt <= 
#>     2.875 ~ "0", .default = "1")), .default = "0")

From there, the Tidy Eval formula can be used anywhere where it can be operated. tidypredict provides three paths:

  • Use directly inside dplyr, mutate(mtcars2, !! tidypredict_fit(model))
  • Use tidypredict_to_column(model) to a piped command set
  • Use tidypredict_sql(model) to retrieve the SQL statement

Categorical predictors

C5.0 handles categorical predictors natively. The generated formula uses %in% for categorical splits, and multi-way splits become nested case_when() branches:

mtcars3 <- mtcars2
mtcars3$gear <- factor(mtcars3$gear)

model_cat <- C5.0(mtcars3[, c("wt", "gear", "mpg")], mtcars3$vs)
tidypredict_fit(model_cat)
#> case_when(mpg <= 21 ~ "0", .default = "1")

parsnip

tidypredict also supports C5.0 model objects fitted via the parsnip package.

library(parsnip)

parsnip_model <- decision_tree(mode = "classification") |>
  set_engine("C5.0") |>
  fit(vs ~ wt + cyl + mpg, data = mtcars2)

tidypredict_fit(parsnip_model)
#> case_when(cyl <= 6 ~ case_when(cyl <= 4 ~ "1", .default = case_when(wt <= 
#>     2.875 ~ "0", .default = "1")), .default = "0")

Limitations

Only single decision trees are supported. Boosted models (trials > 1) and rule-based models (rules = TRUE) cannot be translated and will raise an error.