How to use
tune_grid() and a gam model (
gen_additive_mod()) incl. gam-formula. please provide a code-snippet. thx.
Here's an example:
library(tidymodels) #> Registered S3 method overwritten by 'tune': #> method from #> required_pkgs.model_spec parsnip #> Warning: package 'broom' was built under R version 4.1.2 tidymodels_prefer() gam_spec <- gen_additive_mod(select_features = tune()) %>% set_mode("regression") gam_wflow <- workflow() %>% # smoothing must be specified here: add_model(gam_spec, formula = mpg ~ s(disp) + wt + gear) %>% add_variables(predictors = c(everything()), outcomes = mpg) set.seed(1) car_folds <- bootstraps(mtcars, times = 5) gam_res <- gam_wflow %>% tune_grid(resamples = car_folds) show_best(gam_res, metric = "rmse") #> # A tibble: 2 × 7 #> select_features .metric .estimator mean n std_err .config #> <lgl> <chr> <chr> <dbl> <int> <dbl> <chr> #> 1 FALSE rmse standard 3.48 5 0.848 Preprocessor1_Model2 #> 2 TRUE rmse standard 3.48 5 0.848 Preprocessor1_Model1
Created on 2022-01-24 by the reprex package (v2.0.1)
You can't optimize the smoothing parameters in
tune_grid(). You could set up multiple workflows with different smoothing specifications and use a workflow set to try different ideas.
Thank you Max. Post must be at least 20 characters.
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