Get variable importances

get_variable_importance(models)

Arguments

models

model_list object

Value

Data frame of variables and their importance for predictive power

Details

Some algorithms provide variable importance, others don't. The best-performing model that offers variable importance will be used.

See also

Examples

m <- flash_models(mtcars, outcome = mpg, models = "rf")
#> mpg looks numeric, so training regression algorithms.
#> Training at fixed values: Random Forest
#> #> *** Models successfully trained. The model object contains the training data minus ignored ID columns. *** #> *** If there was PHI in training data, normal PHI protocols apply to the model object. ***
get_variable_importance(m)
#> # A tibble: 10 x 2 #> variable importance #> * <chr> <dbl> #> 1 cyl 100. #> 2 wt 59.5 #> 3 disp 57.5 #> 4 hp 42.6 #> 5 vs 40.2 #> 6 am 18.2 #> 7 drat 9.49 #> 8 gear 9.20 #> 9 carb 8.49 #> 10 qsec 0.