How can I insert a regularization parameter in tidymodels for boost_tree()? In the normal lightgbm package there is the tuning parameter lambda_l1. I would like to use this in tidymodels as well.
I tried to code it like this, but I am unsure if I am doing right:
lgbm_model <-
boost_tree(
mode = "regression",
# mtry = 1,
trees = tune(),
min_n = tune(),
tree_depth = tune(),
learn_rate = tune(),
loss_reduction = tune()
) %>%
set_engine("lightgbm", lambda = 1)
Since lambda_l1 isn't a main argument for boost_tree(), you would indeed supply that argument to set_engine(). Supply it exactly as it is named in lightgbm::lgb.train() or it's param argument, using lambda_l1. The bonsai package, which implements support for the "lightgbm" engine with parsnip, will take care of passing that argument to the right place.
Your code would look something like this:
library(tidymodels)
library(bonsai)
lgb <-
boost_tree(mode = "regression",) %>%
set_engine("lightgbm", lambda_l1 = .9)
lgb_fit <- fit(lgb, mpg ~ ., mtcars)
lgb_fit
#> parsnip model object
#>
#> LightGBM Model (1 tree)
#> Objective: regression
#> Fitted to dataset with 10 columns
To confirm that lambda_l1 was supplied as you intended, you can extract the underlying LightGBM fit and poke inside of it:
lgb_fit_engine <- extract_fit_engine(lgb_fit)
lgb_fit_engine$params$lambda_l1
#> [1] 0.9
Created on 2024-03-28 with reprex v2.1.0
:)