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Random Forest - partykit

Source: vignettes/partykit.Rmd
partykit.Rmd
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(is.na(cyl) ~ NA, cyl <= 6 ~ 22.08, .default = 15.17) + 
#>     case_when(is.na(wt) ~ NA, wt <= 2.78 ~ 27.5428571428571, 
#>         .default = 17.1384615384615) + case_when(is.na(wt) ~ 
#>     NA, wt <= 3.15 ~ 23.7625, .default = 16.5083333333333) + 
#>     case_when(is.na(wt) ~ NA, wt <= 2.78 ~ 26.6285714285714, 
#>         .default = 16.9076923076923) + case_when(is.na(wt) ~ 
#>     NA, wt <= 2.465 ~ 28.3857142857143, .default = 16.7769230769231))/5L

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)
#> (case_when(is.na(cyl) ~ NA, cyl <= 4 ~ 28.275, .default = 16.1) + 
#>     case_when(is.na(wt) ~ NA, wt <= 3.215 ~ 25.2555555555556, 
#>         .default = 16.0272727272727) + case_when(is.na(cyl) ~ 
#>     NA, cyl <= 4 ~ 25.775, .default = 16.675) + case_when(is.na(wt) ~ 
#>     NA, wt <= 3.15 ~ 25.1555555555556, .default = 16.1636363636364) + 
#>     case_when(is.na(wt) ~ NA, wt <= 2.875 ~ 25.9714285714286, 
#>         .default = 17))/5L