randomForest() model

Highlights & Limitations

Uses the randomForest::getTree() to parse each decision path.
In-line functions in the formulas are not supported:
- OK - wt ~ mpg + am
- OK - mutate(mtcars, newam = paste0(am)) and then wt ~ mpg + newam
- Not OK - wt ~ mpg + as.factor(am)
- Not OK - wt ~ mpg + as.character(am)
Interval functions are not supported: tidypredict_interval() & tidypredict_sql_interval()

How it works

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

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

The SQL translations returns a single SQL CASE WHEN operation. Each decision path is a WHEN statement.

library(tidypredict)

tidypredict_sql(model, dbplyr::simulate_mssql())

## <SQL> CASE
## WHEN ((`Petal.Length` <= 2.5)) THEN ('setosa')
## WHEN ((`Petal.Length` > 5.05 AND `Petal.Length` > 2.5)) THEN ('virginica')
## WHEN ((`Petal.Width` > 1.9 AND `Petal.Length` > 2.5 AND `Petal.Length` <= 5.05)) THEN ('virginica')
## WHEN ((`Petal.Length` > 2.5 AND `Sepal.Length` <= 4.95 AND `Petal.Width` <= 1.9 AND `Petal.Length` <= 5.05)) THEN ('virginica')
## WHEN ((`Sepal.Length` > 4.95 AND `Petal.Length` > 2.5 AND `Petal.Width` <= 1.75 AND `Petal.Width` <= 1.9 AND `Petal.Length` <= 5.05)) THEN ('versicolor')
## WHEN ((`Petal.Width` > 1.75 AND `Sepal.Length` > 4.95 AND `Petal.Length` > 2.5 AND `Sepal.Width` <= 3.0 AND `Petal.Width` <= 1.9 AND `Petal.Length` <= 5.05)) THEN ('virginica')
## WHEN ((`Sepal.Width` > 3.0 AND `Petal.Width` > 1.75 AND `Sepal.Length` > 4.95 AND `Petal.Length` > 2.5 AND `Petal.Width` <= 1.9 AND `Petal.Length` <= 5.05)) THEN ('versicolor')
## END

Alternatively, use tidypredict_to_column() if the results are the be used or previewed in dplyr.

iris %>%
  tidypredict_to_column(model) %>%
  head(10)

##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species    fit
## 1           5.1         3.5          1.4         0.2  setosa setosa
## 2           4.9         3.0          1.4         0.2  setosa setosa
## 3           4.7         3.2          1.3         0.2  setosa setosa
## 4           4.6         3.1          1.5         0.2  setosa setosa
## 5           5.0         3.6          1.4         0.2  setosa setosa
## 6           5.4         3.9          1.7         0.4  setosa setosa
## 7           4.6         3.4          1.4         0.3  setosa setosa
## 8           5.0         3.4          1.5         0.2  setosa setosa
## 9           4.4         2.9          1.4         0.2  setosa setosa
## 10          4.9         3.1          1.5         0.1  setosa setosa

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 parsed model contains one row for each path. The field, operator and split_point field list every step in a concatenated character variable.

parse_model(model)

## # A tibble: 8 x 7
##   labels vals         type     estimate field      operator   split_point 
##   <chr>  <chr>        <chr>       <dbl> <chr>      <chr>      <chr>       
## 1 path-1 setosa       path            0 Petal.Len… left       2.5         
## 2 path-2 virginica    path            0 Petal.Len… right{:}r… 5.05{:}2.5  
## 3 path-3 virginica    path            0 Petal.Wid… right{:}l… 1.9{:}5.05{…
## 4 path-4 virginica    path            0 Sepal.Len… left{:}le… 4.95{:}1.9{…
## 5 path-5 versicolor   path            0 Petal.Wid… left{:}ri… 1.75{:}4.95…
## 6 path-6 virginica    path            0 Sepal.Wid… left{:}ri… 3{:}1.75{:}…
## 7 path-7 versicolor   path            0 Sepal.Wid… right{:}r… 3{:}1.75{:}…
## 8 model  randomForest variable       NA <NA>       <NA>       <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)

## case_when((Petal.Length <= 2.5) ~ "setosa", (Petal.Length > 5.05 & 
##     Petal.Length > 2.5) ~ "virginica", (Petal.Width > 1.9 & Petal.Length > 
##     2.5 & Petal.Length <= 5.05) ~ "virginica", (Petal.Length > 
##     2.5 & Sepal.Length <= 4.95 & Petal.Width <= 1.9 & Petal.Length <= 
##     5.05) ~ "virginica", (Sepal.Length > 4.95 & Petal.Length > 
##     2.5 & Petal.Width <= 1.75 & Petal.Width <= 1.9 & Petal.Length <= 
##     5.05) ~ "versicolor", (Petal.Width > 1.75 & Sepal.Length > 
##     4.95 & Petal.Length > 2.5 & Sepal.Width <= 3 & Petal.Width <= 
##     1.9 & Petal.Length <= 5.05) ~ "virginica", (Sepal.Width > 
##     3 & Petal.Width > 1.75 & Sepal.Length > 4.95 & Petal.Length > 
##     2.5 & Petal.Width <= 1.9 & Petal.Length <= 5.05) ~ "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

How it performs

Currently, the formula matches 147 out of 150 prediction of the test model. The threshold in tidypredict_test() is a integer indicating the number of records are OK to be different than the baseline prediction that the predict() function returns.

test <- tidypredict_test(model, iris, threshold = 5)

test

## tidypredict test results
## 
## Success, test is under the set threshold of: 5
## Predictions that did not match predict(): 3

test$raw_results %>%
  filter(predict != tidypredict)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species    predict
## 1          4.9         2.4          3.3         1.0 versicolor versicolor
## 2          6.0         2.7          5.1         1.6 versicolor versicolor
## 3          6.0         2.2          5.0         1.5  virginica  virginica
##   tidypredict
## 1   virginica
## 2   virginica
## 3  versicolor