Skip to content

Random Forest

Source: vignettes/rf.Rmd
rf.Rmd
Function Works
tidypredict_fit(), tidypredict_sql(), parse_model() ✔
tidypredict_to_column() ✗
tidypredict_test() ✗
tidypredict_interval(), tidypredict_sql_interval() ✗
parsnip ✔

How it works

Here is a simple randomForest() model using the iris dataset:

library(dplyr)
library(tidypredict)
library(randomForest)

model <- randomForest(Species ~ ., data = iris, ntree = 100, proximity = TRUE)

Under the hood

The parser is based on the output from the randomForest::getTree() function. It will return as many decision paths as there are non-NA rows in the prediction field.

getTree(model, labelVar = TRUE) %>%
  head()
#>   left daughter right daughter    split var split point status
#> 1             2              3 Petal.Length        2.50      1
#> 2             0              0         <NA>        0.00     -1
#> 3             4              5 Petal.Length        5.05      1
#> 4             6              7  Petal.Width        1.90      1
#> 5             0              0         <NA>        0.00     -1
#> 6             8              9 Sepal.Length        4.95      1
#>   prediction
#> 1       <NA>
#> 2     setosa
#> 3       <NA>
#> 4       <NA>
#> 5  virginica
#> 6       <NA>

The output from parse_model() is transformed into a dplyr, a.k.a Tidy Eval, formula. The entire decision tree becomes one dplyr::case_when() statement

tidypredict_fit(model)[1]
#> `/`()

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

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

parsnip

tidypredict also supports randomForest model objects fitted via the parsnip package.

library(parsnip)

parsnip_model <- rand_forest(mode = "classification") %>%
  set_engine("randomForest") %>%
  fit(Species ~ ., data = iris)

tidypredict_fit(parsnip_model)[[1]]
#> `/`