The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

baguette

R-CMD-check Lifecycle: experimental CRAN status Codecov test coverage

Introduction

The goal of baguette is to provide efficient functions for bagging (aka bootstrap aggregating) ensemble models.

The model objects produced by baguette are kept smaller than they would otherwise be through two operations:

Installation

You can install the released version of baguette from CRAN with:

install.packages("baguette")

Install the development version from GitHub with:

# install.packages("pak")
pak::pak("tidymodels/baguette")

Available Engines

The baguette package provides engines for the models in the following table.

model engine mode
bag_mars earth classification
bag_mars earth regression
bag_mlp nnet classification
bag_mlp nnet regression
bag_tree rpart classification
bag_tree rpart regression
bag_tree C5.0 classification

Example

Let’s build a bagged decision tree model to predict a continuous outcome.

library(baguette)

bag_tree() %>% 
  set_engine("rpart") # C5.0 is also available here
#> Bagged Decision Tree Model Specification (unknown mode)
#> 
#> Main Arguments:
#>   cost_complexity = 0
#>   min_n = 2
#> 
#> Computational engine: rpart

set.seed(123)
bag_cars <- 
  bag_tree() %>% 
  set_engine("rpart", times = 25) %>% # 25 ensemble members 
  set_mode("regression") %>% 
  fit(mpg ~ ., data = mtcars)

bag_cars
#> parsnip model object
#> 
#> Bagged CART (regression with 25 members)
#> 
#> Variable importance scores include:
#> 
#> # A tibble: 10 × 4
#>    term  value std.error  used
#>    <chr> <dbl>     <dbl> <int>
#>  1 disp  905.       51.9    25
#>  2 wt    889.       56.8    25
#>  3 hp    814.       48.7    25
#>  4 cyl   581.       42.9    25
#>  5 drat  540.       54.1    25
#>  6 qsec  281.       53.2    25
#>  7 vs    150.       51.2    20
#>  8 carb   84.4      30.6    25
#>  9 gear   80.0      35.8    23
#> 10 am     51.5      22.9    18

The models also return aggregated variable importance scores.

Contributing

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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.