SHAP (Shapley additive explanations) data object

gg_shap computes SHAP values for a rfsrc or randomForest regression or classification forest by wrapping kernelshap, and reshapes them into a tidy data set with one row per (observation, variable).

Usage

gg_shap(object, newdata, bg_n = 50, which.class = 1, ...)

Arguments

object

A rfsrc or randomForest object (regression or classification).

newdata

Optional data.frame of predictor values to explain (same columns as the model's training predictors). When missing, the model's own training predictors are used.

bg_n

Size of the background/reference sample drawn from the training predictors and passed to kernelshap as bg_X. Larger values are more accurate but slower.

which.class

For classification forests, the class (integer column index into the predicted-probability matrix) whose predicted probability is explained. Defaults to 1.

...

Passed through to kernelshap (e.g. seed, exact, max_iter).

Value

A gg_shap object: a data.frame with columns id (observation index), vars (variable name, an ordered factor ranked by mean absolute SHAP), shap (the signed SHAP contribution), value (numeric feature value, NA for categorical features), and value_label (feature value as character). The background-sample mean prediction is stored in the "baseline" attribute.

See also

kernelshap

Examples

# \donttest{
if (requireNamespace("kernelshap", quietly = TRUE)) {
  rf <- randomForestSRC::rfsrc(Ozone ~ ., data = na.omit(airquality),
                               ntree = 50)
  gg_dta <- gg_shap(rf, bg_n = 20)
}
# }