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Permutation variable importance

Source: R/explain.R
sdt_importance.Rd

How much worse the model gets when each predictor is shuffled. The measure is model-agnostic, so a random forest and a decision tree are ranked on the same scale.

Usage

sdt_importance(x, data = NULL, n_permutations = 10L)

Arguments

x

An sdt_model from sdt_fit(), or an explainer from sdt_explain().

data

Optional data to explain on, passed to sdt_explain().

n_permutations

Number of permutation rounds. More is steadier and slower.

Value

A ggplot2 object.

See also

sdt_profile(), sdt_shap().

Examples

co <- sdt_add_decisions(sdt_synthetic_cohort(n = 200, seed = 3), seed = 4)
needed <- c("parsnip", "recipes", "workflows", "rsample", "tune",
            "yardstick", "rpart", "DALEX", "DALEXtra")
if (all(vapply(needed, requireNamespace, logical(1), quietly = TRUE))) {
  fit <- sdt_fit(co, target = "called_triage", engine = "tree",
                 tune = FALSE, seed = 1, verbose = FALSE)
  sdt_importance(fit)
}