Runs the full explainability pipeline: importance, PDPs, SHAP, correlations.
Usage
xai_pipeline(
learner,
task,
top_k = 10,
n_shap_obs = 5,
cor_threshold = 0.7,
features = NULL,
verbose = TRUE
)Arguments
- learner
A trained mlr3 learner
- task
The mlr3 task
- top_k
Number of top features to analyze in detail (default: 10)
- n_shap_obs
Number of observations for SHAP (default: 5)
- cor_threshold
Correlation threshold for warnings (default: 0.7)
- features
Optional: pre-selected feature subset
- verbose
Print progress (default: TRUE)
Examples
if (FALSE) { # \dontrun{
if (requireNamespace("DALEX", quietly = TRUE) &&
requireNamespace("ggplot2", quietly = TRUE)) {
results <- xai_pipeline(learner, task, top_k = 10)
plot(results$importance)
print(results$correlations$high_cor_pairs)
}
} # }