bagger models
In tidypredict: Run Predictions Inside the Database

if (requireNamespace("baguette", quietly = TRUE)) {
  library(tidypredict)
  library(dplyr)
  eval_code <- TRUE
} else {
  eval_code <- FALSE
}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = eval_code
)

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

baguette::bagger() fits an ensemble of models on bootstrap samples of the training data. The "CART" base model, which fits rpart::rpart() trees, and the "C5.0" base model, which fits C50::C5.0() trees, are supported. tidypredict_fit() returns one nested case_when() per tree, so the size of the returned expression grows with times.

For regression models the fitted value is the mean of the individual tree predictions. For classification models the class probabilities of each tree are averaged, and the returned expression is the class with the largest average probability.

`tidypredict_` functions

set.seed(100)
model <- baguette::bagger(mpg ~ wt + cyl + disp, data = mtcars, times = 5)

Create the R formula r tidypredict_fit(model)
Add the predictions to the original table r mtcars %>% tidypredict_to_column(model) %>% glimpse()
Confirm that the results match the model's predict() results r tidypredict_test(model, mtcars)
Get the SQL translation r tidypredict_sql(model, dbplyr::simulate_mssql())

Classification

set.seed(100)
model <- baguette::bagger(Species ~ ., data = iris, times = 3)

tidypredict_test(model, iris)

C5.0 trees are only fit for classification, and are used by passing base_model = "C5.0".

set.seed(100)
model <- baguette::bagger(
  Species ~ .,
  data = iris,
  base_model = "C5.0",
  times = 3
)

tidypredict_test(model, iris)

parsnip

Models fit with parsnip::bag_tree() and the "rpart" or "C5.0" engine are supported as well.

library(parsnip)

set.seed(100)
model <- bag_tree(mode = "regression") %>%
  set_engine("rpart", times = 5) %>%
  fit(mpg ~ wt + cyl + disp, data = mtcars)

tidypredict_fit(model)