Feature importance

Returns a tidy feature-importance table. For coefficient-based native models, importance is the absolute coefficient magnitude after the model's internal scaling. For simple tree models, the selected split variable receives the split contrast magnitude.

Usage

importance(
  x,
  model = NULL,
  scope = NULL,
  subject = NULL,
  n = NULL,
  method = c("coefficient", "permutation"),
  repeats = 5L,
  ...
)

Arguments

x

An idiographic fit.

model, scope, subject

Optional filters.

n

Optional number of rows.

method, repeats

Only for importance(). "coefficient" (the default) uses the size of each standardized coefficient, which only describes models that have coefficients. "permutation" shuffles each predictor in the held-out rows and reports how much worse the model gets – model-agnostic, so it also explains knn, forests, kernel machines and boosted ensembles, which otherwise return nothing at all; repeats is the number of shuffles.

...

Ignored.

Value

A tidy data frame.