---
title: "Random Forest"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Random Forest}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r pre, include = FALSE}
if (!rlang::is_installed("randomForest")) {
  knitr::opts_chunk$set(
    eval = FALSE
  )
}
```

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(dplyr)
library(tidypredict)
library(randomForest)
library(parsnip)
set.seed(100)
```

| 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 `mtcars` dataset:

```{r}
library(dplyr)
library(tidypredict)
library(randomForest)

model <- randomForest(mpg ~ ., data = mtcars, ntree = 5, 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.
```{r}
getTree(model, labelVar = TRUE) %>%
  head()
```


The output from `parse_model()` is transformed into a `dplyr`, a.k.a Tidy Eval, formula. Each decision tree becomes one `dplyr::case_when()` statement, which are then combined.
```{r}
tidypredict_fit(model)
```

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.

```{r}
library(parsnip)

parsnip_model <- rand_forest(mode = "regression", trees = 5) %>%
  set_engine("randomForest") %>%
  fit(mpg ~ ., data = mtcars)

tidypredict_fit(parsnip_model)
```