One weight per channel, for the area of applicability, from an importance.

The dissimilarity index measures how far a pixel is from the training data, one axis per channel. Unweighted, a channel the model ignores counts as much as the one it leans on, and a pixel unlike the training data in an ignored channel falls outside the area of applicability for nothing. Meyer & Pebesma (2021) weight each axis by the predictor's importance; this gives each channel its variable's importance – every channel of a one-hot set, or of a group of yours, its variable's – and a negative importance, noise around zero, weight zero.

Usage

importance_weights(x)

Arguments

x

A dsm_importance with one value per variable: by permutation_importance(), shap_importance(), sage_importance(), refit_importance() or ale_effect().

Value

A named numeric vector, one weight per channel in the store's order, for dsm_predict(aoa_weights = ) or aoa_reference(weights = ).

Examples

if (FALSE) { # torch::torch_is_installed()
# \donttest{
run <- example_run()    # a small fitted run, made once a session
imp <- dsm_importance(run$final, run$data, shap_importance(samples = 20, background = 20),
                      verbose = FALSE)
importance_weights(imp)   # for dsm_predict(aoa_weights = ) or aoa_reference(weights = )
# }
}