Model-based boosting, using mboost

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

How it works

mboost::blackboost() boosts regression trees. Each boosting iteration contributes one tree, shrunk by the learning rate nu, and the contributions are added to the model’s offset.

library(mboost)
library(dplyr)
library(tidypredict)

model <- blackboost(
  mpg ~ wt + cyl,
  data = mtcars,
  control = boost_control(mstop = 10)
)

Under the hood

The parser walks the partykit tree behind every boosting iteration and turns it into one nested dplyr::case_when() statement. The learning rate and the offset are folded into the result.

pm <- parse_model(model)
str(pm, 2)
#> List of 2
#>  $ general       :List of 5
#>   ..$ model  : chr "blackboost"
#>   ..$ type   : chr "tree"
#>   ..$ version: num 3
#>   ..$ nu     : num 0.1
#>   ..$ offset : num 20.1
#>  $ tree_info_list:List of 10
#>   ..$ :List of 10
#>   ..$ :List of 10
#>   ..$ :List of 10
#>   ..$ :List of 10
#>   ..$ :List of 10
#>   ..$ :List of 10
#>   ..$ :List of 10
#>   ..$ :List of 10
#>   ..$ :List of 10
#>   ..$ :List of 10
#>  - attr(*, "class")= chr [1:3] "parsed_model" "pm_tree" "list"

The parsed model is transformed into a dplyr, a.k.a. Tidy Eval, formula.

tidypredict_fit(model)
#> 20.090625 + 0.1 * (case_when(is.na(wt) ~ NA, wt <= 2.2 ~ 9.97604166666667, 
#>     .default = case_when(is.na(cyl) ~ NA, cyl <= 6 ~ 0.834375000000004, 
#>         .default = -4.990625)) + case_when(is.na(wt) ~ NA, wt <= 
#>     2.2 ~ 8.9784375, .default = case_when(is.na(cyl) ~ NA, cyl <= 
#>     6 ~ 0.750937500000005, .default = -4.49156249999999)) + case_when(is.na(wt) ~ 
#>     NA, wt <= 2.2 ~ 8.08059375, .default = case_when(is.na(cyl) ~ 
#>     NA, cyl <= 6 ~ 0.675843750000005, .default = -4.04240625)) + 
#>     case_when(is.na(wt) ~ NA, wt <= 2.2 ~ 7.272534375, .default = case_when(is.na(wt) ~ 
#>         NA, wt <= 3.215 ~ 1.02111687500001, .default = -3.3653984375)) + 
#>     case_when(is.na(wt) ~ NA, wt <= 2.32 ~ 5.95040475892858, 
#>         .default = case_when(is.na(wt) ~ NA, wt <= 3.215 ~ 0.756544909722228, 
#>             .default = -3.02885859375)) + case_when(is.na(wt) ~ 
#>     NA, wt <= 2.2 ~ 5.95024046160714, .default = case_when(is.na(wt) ~ 
#>     NA, wt <= 3.44 ~ 0.233000806632658, .default = -3.24695450520833)) + 
#>     case_when(is.na(wt) ~ NA, wt <= 2.2 ~ 5.35521641544643, .default = case_when(is.na(wt) ~ 
#>         NA, wt <= 3.215 ~ 0.768112017372454, .default = -2.48827616665019)) + 
#>     case_when(is.na(wt) ~ NA, wt <= 2.2 ~ 4.81969477390179, .default = case_when(is.na(wt) ~ 
#>         NA, wt <= 3.46 ~ 0.143947783877997, .default = -2.82521685468915)) + 
#>     case_when(is.na(wt) ~ NA, wt <= 2.32 ~ 3.9568504185555, .default = case_when(is.na(wt) ~ 
#>         NA, wt <= 3.845 ~ -0.506055821571786, .default = -4.26769516922023)) + 
#>     case_when(is.na(wt) ~ NA, wt <= 3.215 ~ case_when(is.na(cyl) ~ 
#>         NA, cyl <= 4 ~ 2.98132077349875, .default = -0.46269228210335), 
#>         .default = case_when(is.na(wt) ~ NA, wt <= 3.845 ~ -1.25978037406472, 
#>             .default = -3.84092565229821)))

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

mtcars %>%
  tidypredict_to_column(model) %>%
  glimpse()
#> Rows: 32
#> Columns: 12
#> $ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,…
#> $ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8,…
#> $ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 16…
#> $ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180…
#> $ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,…
#> $ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.…
#> $ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18…
#> $ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,…
#> $ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,…
#> $ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3,…
#> $ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2,…
#> $ fit  <dbl> 20.51214, 20.51214, 21.82222, 20.51214, 17.71102, 18.94160, 17.06…

How it performs

tidypredict_test(model, df = mtcars)
#> tidypredict test results
#> Difference threshold: 1e-12
#> 
#>  All results are within the difference threshold

Categorical predictors

blackboost() handles factors natively, and the generated formula uses %in% for the resulting splits.

df <- transform(mtcars, cyl = factor(cyl))

model_cat <- blackboost(
  mpg ~ wt + cyl,
  data = df,
  control = boost_control(mstop = 10)
)

tidypredict_test(model_cat, df = df)
#> tidypredict test results
#> Difference threshold: 1e-12
#> 
#>  All results are within the difference threshold

Limitations