The R brulee
package contains several basic modeling functions that
use the torch
package infrastructure, such as:
You can install the released version of brulee from CRAN with:
install.packages("brulee")
And the development version from GitHub with:
# install.packages("pak")
pak::pak("tidymodels/brulee")
brulee
has formula, x/y, and recipe user interfaces for each function.
For example:
library(brulee)
library(recipes)
library(yardstick)
data(bivariate, package = "modeldata")
set.seed(20)
nn_log_biv <- brulee_mlp(Class ~ log(A) + log(B), data = bivariate_train,
epochs = 150, hidden_units = 3)
# We use the tidymodels semantics to always return a tibble when predicting
predict(nn_log_biv, bivariate_test, type = "prob") %>%
bind_cols(bivariate_test) %>%
roc_auc(Class, .pred_One)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 roc_auc binary 0.837
A recipe can also be used if the data require some sort of preprocessing (e.g., indicator variables, transformations, or standardization):
library(recipes)
rec <-
recipe(Class ~ ., data = bivariate_train) %>%
step_YeoJohnson(all_numeric_predictors()) %>%
step_normalize(all_numeric_predictors())
set.seed(20)
nn_rec_biv <- brulee_mlp(rec, data = bivariate_train,
epochs = 150, hidden_units = 3)
# A little better
predict(nn_rec_biv, bivariate_test, type = "prob") %>%
bind_cols(bivariate_test) %>%
roc_auc(Class, .pred_One)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 roc_auc binary 0.866
Please note that the brulee project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
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