We’re chuffed to announce the release of usemodels 0.2.0. The usemodels package enables users to generate tidymodels code for fitting and tuning models. Given a) a formula and b) a data set, the use_*() functions (such as use_glmnet() and use_xgboost()) create code to fit that specific model to that data, including appropriate preprocessing.

You can install it from CRAN with:

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install.packages("usemodels")

This blog post describes some new features. You can see a full list of changes in the release notes .

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library(usemodels)

Clipboard access#

Each of the use_*() functions now has a clipboard feature that will send the new code to the clipboard, instead of writing to the console window.

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use_cubist(mpg ~ ., data = mtcars, clipboard = TRUE)

## ✓ code is on the clipboard.

New models#

As requested in GitHub issues, support for C5.0 and SVM models was added. SVM models require centering and scaling of the predictors, so the usemodel function provides this automatically:

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data(two_class_dat, package = "modeldata")
use_kernlab_svm_rbf(Class ~ ., data = two_class_dat)

## kernlab_recipe <- 
##   recipe(formula = Class ~ ., data = two_class_dat) %>% 
##   step_zv(all_predictors()) %>% 
##   step_normalize(all_numeric_predictors()) 
## 
## kernlab_spec <- 
##   svm_rbf(cost = tune(), rbf_sigma = tune()) %>% 
##   set_mode("classification") 
## 
## kernlab_workflow <- 
##   workflow() %>% 
##   add_recipe(kernlab_recipe) %>% 
##   add_model(kernlab_spec) 
## 
## set.seed(81161)
## kernlab_tune <-
##   tune_grid(kernlab_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))

Let us know if there are other features that would be interesting for the package on its GitHub issues page .

Acknowledgements#

Thanks to all the people who contributed to usemodels since our last blog post : @amazongodman , @brshallo , @bryceroney , @czeildi , @EmilHvitfeldt , @hfrick , @jennybc , @juliasilge , @larry77 , and @topepo .