interpret.Rd
Interpret a model via regularized coefficient estimates
interpret(x, sparsity = NULL, remove_zeros = TRUE, top_n)
x | a model_list object containing a glmnet model |
---|---|
sparsity | If NULL (default) coefficients for the best-performing model will be returned. Otherwise, a value in [0, 1] that determines the sparseness of the model for which coefficients will be returned, with 0 being maximally sparse (i.e. having the fewest non-zero coefficients) and 1 being minimally sparse |
remove_zeros | Remove features with coefficients equal to 0? Default is TRUE |
top_n | Integer: How many coefficients to return? The largest top_n absolute-value coefficients will be returned. If missing (default), all coefficients are returned |
A data frame of variables and their regularized regression coefficient estimates with parent class "interpret"
**WARNING** Coefficients are on the scale of the predictors; they
are not standardized, so unless features were scaled before training (e.g.
with prep_data(..., scale = TRUE)
, the magnitude of coefficients
does not necessarily reflect their importance.
If x was trained with more than one value of alpha the best value of alpha is used; sparsity is determined only via the selection of lambda. Using only lasso regression (i.e. alpha = 1) will produce a sparser set of coefficients and can be obtained by not tuning hyperparameters.
#>#>#> #>#> #>#>#>#> #>#>interpret(m)#> # A tibble: 12 x 2 #> variable coefficient #> * <chr> <dbl> #> 1 (Intercept) -5.89 #> 2 weight_class_normal -1.49 #> 3 pedigree 0.785 #> 4 weight_class_other -0.726 #> 5 weight_class_overweight -0.663 #> 6 pregnancies 0.0912 #> 7 weight_class_obese 0.0662 #> 8 plasma_glucose 0.0301 #> 9 skinfold 0.0153 #> 10 age 0.0126 #> 11 diastolic_bp 0.00147 #> 12 insulin 0.000188interpret(m, .2)#> # A tibble: 12 x 2 #> variable coefficient #> * <chr> <dbl> #> 1 (Intercept) -0.691 #> 2 weight_class_normal -0.00873 #> 3 pedigree 0.00677 #> 4 weight_class_other -0.00593 #> 5 weight_class_obese 0.00469 #> 6 weight_class_overweight -0.00441 #> 7 pregnancies 0.000847 #> 8 skinfold 0.000314 #> 9 age 0.000260 #> 10 plasma_glucose 0.000209 #> 11 diastolic_bp 0.000176 #> 12 insulin 0.0000324interpret(m) %>% plot()