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Random Forest, using Ranger

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 terminal
#> 1      0         1          2          3  Petal.Width     0.75    FALSE
#> 2      1        NA         NA         NA         <NA>       NA     TRUE
#> 3      2         3          4          3  Petal.Width     1.75    FALSE
#> 4      3         5          6          0 Sepal.Length     7.10    FALSE
#> 5      4        NA         NA         NA         <NA>       NA     TRUE
#> 6      5         7          8          3  Petal.Width     1.65    FALSE
#>   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]
#> [[1]]
#> case_when(Petal.Width < 0.75 ~ "setosa", Petal.Width >= 1.75 & 
#>     Petal.Width >= 0.75 ~ "virginica", Sepal.Length >= 7.1 & 
#>     Petal.Width < 1.75 & Petal.Width >= 0.75 ~ "virginica", Petal.Length >= 
#>     5.35 & Petal.Width < 1.65 & Sepal.Length < 7.1 & Petal.Width < 
#>     1.75 & Petal.Width >= 0.75 ~ "virginica", Sepal.Width < 2.75 & 
#>     Petal.Width >= 1.65 & Sepal.Length < 7.1 & Petal.Width < 
#>     1.75 & Petal.Width >= 0.75 ~ "virginica", Sepal.Width >= 
#>     2.75 & Petal.Width >= 1.65 & Sepal.Length < 7.1 & Petal.Width < 
#>     1.75 & Petal.Width >= 0.75 ~ "versicolor", Petal.Width < 
#>     1.45 & Petal.Length < 5.35 & Petal.Width < 1.65 & Sepal.Length < 
#>     7.1 & Petal.Width < 1.75 & Petal.Width >= 0.75 ~ "versicolor", 
#>     Petal.Length < 5 & Petal.Width >= 1.45 & Petal.Length < 5.35 & 
#>         Petal.Width < 1.65 & Sepal.Length < 7.1 & Petal.Width < 
#>         1.75 & Petal.Width >= 0.75 ~ "versicolor", Sepal.Length < 
#>         6.15 & Petal.Length >= 5 & Petal.Width >= 1.45 & Petal.Length < 
#>         5.35 & Petal.Width < 1.65 & Sepal.Length < 7.1 & Petal.Width < 
#>         1.75 & Petal.Width >= 0.75 ~ "versicolor", Sepal.Length >= 
#>         6.15 & Petal.Length >= 5 & Petal.Width >= 1.45 & Petal.Length < 
#>         5.35 & Petal.Width < 1.65 & Sepal.Length < 7.1 & Petal.Width < 
#>         1.75 & Petal.Width >= 0.75 ~ "virginica")

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

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]]
#> case_when(Petal.Width < 0.7 ~ "setosa", Petal.Length < 4.85 & 
#>     Petal.Width >= 0.7 ~ "versicolor", Petal.Width < 1.75 & Petal.Length >= 
#>     4.85 & Petal.Width >= 0.7 ~ "versicolor", Petal.Width >= 
#>     1.75 & Petal.Length >= 4.85 & Petal.Width >= 0.7 ~ "virginica")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.