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.
- feature importance
- shap
- permutation
- interpretability
- ml
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.
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