Random Forest - partykit • tidypredict

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)
#> (case_when(cyl <= 6 ~ 22.08, .default = 15.17) + case_when(wt <= 
#>     2.78 ~ 27.5428571428571, .default = 17.1384615384615) + case_when(wt <= 
#>     3.15 ~ 23.7625, .default = 16.5083333333333) + case_when(wt <= 
#>     2.78 ~ 26.6285714285714, .default = 16.9076923076923) + case_when(wt <= 
#>     2.465 ~ 28.3857142857143, .default = 16.7769230769231))/5L

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

Use directly inside dplyr, mutate(mtcars, !!tidypredict_fit(model))
Use tidypredict_to_column(model) 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)
#> (case_when(cyl <= 4 ~ 28.275, .default = 16.1) + case_when(wt <= 
#>     3.215 ~ 25.2555555555556, .default = 16.0272727272727) + 
#>     case_when(cyl <= 4 ~ 25.775, .default = 16.675) + case_when(wt <= 
#>     3.15 ~ 25.1555555555556, .default = 16.1636363636364) + case_when(wt <= 
#>     2.875 ~ 25.9714285714286, .default = 17))/5L