Random Forest - partykit
In tidypredict: Run Predictions Inside the Database

if (!rlang::is_installed("partykit")) {
  knitr::opts_chunk$set(
    eval = FALSE
  )
}

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(dplyr)
library(tidypredict)
library(partykit)
library(parsnip)
set.seed(100)

| Function |Works| |---------------------------------------------------------------|-----| |tidypredict_fit(), tidypredict_sql(), parse_model() | ✔ | |tidypredict_to_column() | ✔ | |tidypredict_test() | ✔ | |tidypredict_interval(), tidypredict_sql_interval() | ✗ | |parsnip | ✔ |

Only regression models are supported. Classification cforest() models rely on a voting mechanism that cannot be expressed as a single formula.

How it works

Here is a simple cforest() model using the mtcars dataset:

library(dplyr)
library(tidypredict)
library(partykit)

model <- cforest(mpg ~ wt + cyl, data = mtcars, ntree = 5)

Under the hood

Each tree in the forest is a partykit party tree. tidypredict turns every tree into a nested dplyr::case_when() statement using each terminal node's in-bag weighted mean, then averages the trees. This matches the default predict() behavior, which scales the per-tree weights before aggregating.

tidypredict_fit(model)

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

Use directly inside dplyr, mutate(mtcars, !!tidypredict_fit(model))
Use tidypredict_to_column(model) to add it to a piped command set
Use tidypredict_sql(model, con) to retrieve the SQL statement

parsnip

tidypredict also supports cforest model objects fitted via the parsnip package with the "partykit" engine, which is provided by the bonsai package.

library(bonsai)
library(parsnip)

parsnip_model <- rand_forest(mode = "regression", trees = 5) %>%
  set_engine("partykit") %>%
  fit(mpg ~ wt + cyl, data = mtcars)

tidypredict_fit(parsnip_model)