feature_importance

Subject

Sampling, Validation and ML Diagnostics

Why This Module Exists

Improves model interpretability and helps remove unstable or redundant features.

Mathematical Foundations

MDI

$I_j=\sum_{t\in T_j} p(t)\Delta i(t)$

MDA

$I_j=Score(X)-Score(X_{perm(j)})$

Usage Examples

Rust

Run MDA with classifier

use openquant::feature_importance::mean_decrease_accuracy;

// Plug in your classifier implementing SimpleClassifier
let importance = mean_decrease_accuracy(&clf, &x, &y, 5)?;

API Reference

Rust API

• mean_decrease_impurity
• mean_decrease_accuracy
• single_feature_importance
• feature_pca_analysis

Implementation Notes

• Cross-validated MDA is preferred when leakage risk is high.
• Compare ranking stability across folds/time windows.