# 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")