Random Forest

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 prediction
#> 1             2              3 Petal.Length        2.50      1       <NA>
#> 2             0              0         <NA>        0.00     -1     setosa
#> 3             4              5 Petal.Length        5.05      1       <NA>
#> 4             6              7  Petal.Width        1.90      1       <NA>
#> 5             0              0         <NA>        0.00     -1  virginica
#> 6             8              9 Sepal.Length        4.95      1       <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.Length < 2.5 ~ "setosa", Petal.Length >= 5.05 & 
#>     Petal.Length >= 2.5 ~ "virginica", Petal.Width >= 1.9 & Petal.Length < 
#>     5.05 & Petal.Length >= 2.5 ~ "virginica", Sepal.Length < 
#>     4.95 & Petal.Width < 1.9 & Petal.Length < 5.05 & Petal.Length >= 
#>     2.5 ~ "virginica", Petal.Width < 1.75 & Sepal.Length >= 4.95 & 
#>     Petal.Width < 1.9 & Petal.Length < 5.05 & Petal.Length >= 
#>     2.5 ~ "versicolor", Sepal.Width < 3 & Petal.Width >= 1.75 & 
#>     Sepal.Length >= 4.95 & Petal.Width < 1.9 & Petal.Length < 
#>     5.05 & Petal.Length >= 2.5 ~ "virginica", Sepal.Width >= 
#>     3 & Petal.Width >= 1.75 & Sepal.Length >= 4.95 & Petal.Width < 
#>     1.9 & Petal.Length < 5.05 & Petal.Length >= 2.5 ~ "versicolor")

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]]
#> case_when(Petal.Length < 2.45 & Sepal.Length < 5.45 ~ "setosa", 
#>     Petal.Width < 1.6 & Petal.Length >= 2.45 & Sepal.Length < 
#>         5.45 ~ "versicolor", Petal.Width >= 1.6 & Petal.Length >= 
#>         2.45 & Sepal.Length < 5.45 ~ "virginica", Petal.Length >= 
#>         4.7 & Sepal.Length < 5.75 & Sepal.Length >= 5.45 ~ "virginica", 
#>     Petal.Width < 0.65 & Petal.Length < 4.7 & Sepal.Length < 
#>         5.75 & Sepal.Length >= 5.45 ~ "setosa", Petal.Width >= 
#>         0.65 & Petal.Length < 4.7 & Sepal.Length < 5.75 & Sepal.Length >= 
#>         5.45 ~ "versicolor", Petal.Length < 2.55 & Petal.Length < 
#>         4.95 & Sepal.Length >= 5.75 & Sepal.Length >= 5.45 ~ 
#>         "setosa", Petal.Length >= 5.05 & Petal.Length >= 4.95 & 
#>         Sepal.Length >= 5.75 & Sepal.Length >= 5.45 ~ "virginica", 
#>     Petal.Width < 1.7 & Petal.Length >= 2.55 & Petal.Length < 
#>         4.95 & Sepal.Length >= 5.75 & Sepal.Length >= 5.45 ~ 
#>         "versicolor", Petal.Width >= 1.7 & Petal.Length >= 2.55 & 
#>         Petal.Length < 4.95 & Sepal.Length >= 5.75 & Sepal.Length >= 
#>         5.45 ~ "virginica", Sepal.Length < 6.5 & Petal.Length < 
#>         5.05 & Petal.Length >= 4.95 & Sepal.Length >= 5.75 & 
#>         Sepal.Length >= 5.45 ~ "virginica", Sepal.Length >= 6.5 & 
#>         Petal.Length < 5.05 & Petal.Length >= 4.95 & Sepal.Length >= 
#>         5.75 & Sepal.Length >= 5.45 ~ "versicolor")