Feature Importance Computation Via Gain Split And Cover Metrics

via “feature importance computation via gain, split, and cover metrics”

LightGBM Python-package

Unique: Three complementary importance metrics (gain, split, cover) computed directly from tree structure during training, enabling lightweight importance computation without additional inference passes

vs others: Faster than SHAP-based importance computation; more interpretable than permutation importance for tree-based models