Random Forest - partykit

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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.