Guides ยท Technology

ML Feature Importance Basics

Interpret models cautiously

Assessing feature importance uses methods like permutation, SHAP, or coefficients to see influence on predictions; results depend on data and correlations, so validate and avoid over-trusting single metrics.

Choose Method

Permutation/SHAP for complex models; coefficients for linear/logistic.

Beware Correlations

Correlated features can distort rankings; use domain sense.

Validate

Compare across splits; use to guide but not solely decide changes.

Keep Exploring

Related Terms

One useful idea at a time

Get new explainers in your inbox

Occasional clear explanations. No daily noise.