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| <!-- README.md is generated from README.Rmd. Please edit that file --> | ||
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| # baguette | ||
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| <!-- badges: start --> | ||
| [](https://www.tidyverse.org/lifecycle/#experimental) | ||
| [](https://cran.r-project.org/package=baguette) | ||
| [](https://codecov.io/gh/tidymodels/baguette?branch=master) | ||
| [](https://github.com/tidymodels/baguette/actions) | ||
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| [](https://www.tidyverse.org/lifecycle/#experimental) | ||
| [](https://cran.r-project.org/package=baguette) | ||
| [](https://codecov.io/gh/tidymodels/baguette?branch=master) | ||
| [](https://github.com/tidymodels/baguette/actions) | ||
| [](https://github.com/tidymodels/baguette/actions) | ||
| <!-- badges: end --> | ||
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| The goal of baguette is to provide efficient functions that can be used to create ensemble models via bagging. | ||
| ## Introduction | ||
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| The goal of baguette is to provide efficient functions for bagging (aka | ||
| [bootstrap | ||
| aggregating](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C7&q=bagging+predictors+breiman+1996&oq=Bagging+predictors+)) | ||
| ensemble models. | ||
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| The model objects produced by baguette are kept smaller than they would | ||
| otherwise be through two operations: | ||
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| - The [butcher](https://tidymodels.github.io/butcher/) package is used | ||
| to remove object elements that are not crucial to using the models. | ||
| For example, some models contain copies of the training set or model | ||
| residuals when created. These are removed to save space. | ||
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| - For ensembles whose base models use a formula method, there is a | ||
| built-in redundancy because each model has an identical terms | ||
| object. However, each one of these takes up separate space in memory | ||
| and can be quite large when there are many predictors. The baguette | ||
| package solves this problem by replacing each terms object with the | ||
| object from the first model in the ensemble. Since the other terms | ||
| objects are not modified, we get the same functional capabilities | ||
| using far less memory to save the ensemble. | ||
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| ## Installation | ||
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| For now: | ||
| You can install the released version of baguette from | ||
| [CRAN](https://CRAN.R-project.org) with: | ||
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| ``` r | ||
| install.packages("baguette") | ||
| ``` | ||
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| Install the development version from GitHub with: | ||
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| ``` r | ||
| require("devtools") | ||
| install_github("tidymodels/baguette") | ||
| ``` | ||
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| ## Example | ||
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| Let’s build a bagged decision tree model to predict a continuous | ||
| outcome. | ||
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| ``` r | ||
| devtools::install_github("tidymodels/baguette") | ||
| library(baguette) | ||
| #> Loading required package: parsnip | ||
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| bag_tree() %>% | ||
| set_engine("rpart") # C5.0 is also available here | ||
| #> Bagged Decision Tree Model Specification (unknown) | ||
| #> | ||
| #> Main Arguments: | ||
| #> cost_complexity = 0 | ||
| #> min_n = 2 | ||
| #> | ||
| #> Computational engine: rpart | ||
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| set.seed(123) | ||
| bag_cars <- | ||
| bag_tree() %>% | ||
| set_engine("rpart", times = 25) %>% # 25 ensemble members | ||
| set_mode("regression") %>% | ||
| fit(mpg ~ ., data = mtcars) | ||
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| bag_cars | ||
| #> parsnip model object | ||
| #> | ||
| #> Fit time: 3.6s | ||
| #> Bagged CART (regression with 25 members) | ||
| #> | ||
| #> Variable importance scores include: | ||
| #> | ||
| #> # A tibble: 10 x 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 | ||
| ``` | ||
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| The models also return aggregated variable importance scores. | ||
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| ## Contributing | ||
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| This project is released with a [Contributor Code of | ||
| Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). | ||
| By contributing to this project, you agree to abide by its terms. | ||
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| - For questions and discussions about tidymodels packages, modeling, | ||
| and machine learning, please [post on RStudio | ||
| Community](https://rstd.io/tidymodels-community). | ||
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| - If you think you have encountered a bug, please [submit an | ||
| issue](https://github.com/tidymodels/baguette/issues). | ||
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| - Either way, learn how to create and share a | ||
| [reprex](https://rstd.io/reprex) (a minimal, reproducible example), | ||
| to clearly communicate about your code. | ||
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| - Check out further details on [contributing guidelines for tidymodels | ||
| packages](https://www.tidymodels.org/contribute/) and [how to get | ||
| help](https://www.tidymodels.org/help/). |
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