Random Forest, using Ranger • tidypredict

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 ranger() model using the iris dataset:

library(dplyr)
library(tidypredict)
library(ranger)

model <- ranger(Species ~ ., data = iris, num.trees = 100)

Under the hood

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

treeInfo(model) %>%
  head()
#>   nodeID leftChild rightChild splitvarID splitvarName splitval
#> 1      0         1          2          3  Petal.Width     1.75
#> 2      1         3          4          2 Petal.Length     2.45
#> 3      2         5          6          2 Petal.Length     4.85
#> 4      3        NA         NA         NA         <NA>       NA
#> 5      4         7          8          2 Petal.Length     5.40
#> 6      5        NA         NA         NA         <NA>       NA
#>   terminal prediction
#> 1    FALSE       <NA>
#> 2    FALSE       <NA>
#> 3    FALSE       <NA>
#> 4     TRUE     setosa
#> 5    FALSE       <NA>
#> 6     TRUE  virginica

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 ranger model objects fitted via the parsnip package.

library(parsnip)

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

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