Several importances, side by side.

Aligns importances computed by different methods, variants, rows or runs – permutation over all rows against within blocks, the test set against the folds, the spatial design against the random one – by what each perturbed (the variable, for a permutation), and says how far their rankings agree. Where they disagree is the reading: a variable that keeps its importance within blocks discriminates locally; one that loses it carries a regional gradient.

Usage

compare_importance(..., n = 20L)

Arguments

...

Two or more dsm_importance, named or not. Names become the labels; unnamed ones are labelled by run, rows and method.

n

How many variables the print shows.

Value

An importance_comparison: table (one row per target: each importance, its rank, and its share of that importance's largest), agreement (Spearman's correlation between the rankings, over the variables they share), labels, and for two SHAP importances of the same points points_agreement (per variable, the correlation of their values point by point and their mean difference against the second's size).

Examples

if (FALSE) { # torch::torch_is_installed()
# \donttest{
run <- example_run()    # a small fitted run, made once a session
perm <- dsm_importance(run$final, run$data, permutation_importance(draws = 2),
                       verbose = FALSE)
shap <- dsm_importance(run$final, run$data, shap_importance(samples = 20, background = 20),
                       verbose = FALSE)
compare_importance(permutation = perm, shap = shap)
# }
}