A model-importance chart can be mistaken for a list of physical causes. Permutation importance instead examines how a fitted model's score changes when a feature is shuffled.
Scikit-learn explains permutation importance and cautions about correlated features. Scikit-learn — permutation importance.
Interpret the model's dependence
For a product-record model, two related fields may carry similar information. Shuffling one may have little effect because the other remains available. A small importance value does not automatically mean the underlying concept is irrelevant.
Avoid causal language
A predictive association is not proof that changing a material or process will change the outcome. Keep the fitted model, evaluation set and scoring method with the explanation. Use additional evidence for production decisions about causes.
Source reviewed 25 October 2026. Interpretation and original diagrams: PLUSHIP / Qunhe. Examples are illustrative.
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