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
Value
A data frame of variables and their regularized regression
coefficient estimates with parent class "interpret"
Details
**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.