Correlated features can mask one another, so permuting a single feature may not disrupt the model enough to show its full contribution. The model may still recover the same signal from a related feature. Grouping correlated features and permuting them together gives a more accurate view of how much the shared signal actually matters.
Why correlated features dilute permutation importance
Permutation importance measures how much model performance drops when one feature is shuffled. With correlated features, that single shuffle often leaves the model with a substitute signal, so the score for each feature can look artificially small. The result is not that the feature is unimportant, but that its contribution is shared across a correlated group.
In practice, this is a feature attribution problem, not a model quality problem. When two or more inputs carry overlapping information, the model can rely on either one and still preserve much of its predictive power. That makes standard one-at-a-time permutation sensitive to redundancy, because the importance estimate reflects the leftover signal the model can still recover.
The same issue shows up even when correlations are only moderate. If one feature is noisy, partially missing, or derived from the same source as another feature, the model may distribute weight unevenly across them. A feature can therefore be genuinely influential while appearing weak under permutation because another feature continues to carry the predictive pattern.
Why grouped permutation is often a better test
Grouping correlated features and permuting them together breaks the shared signal rather than just one piece of it. That usually gives a truer estimate of the collective contribution, especially when the features are proxies for the same underlying construct. It also helps distinguish redundancy from genuinely independent predictive value.
A grouped approach is most useful when you want to answer, “How much does this information source matter?” rather than “Which individual column matters most?” That distinction matters in model interpretation, feature selection, and stakeholder reporting, because a weak individual permutation score can hide a strong shared dependency across the feature set.
There is no universal standard for how to define groups. Current guidance suggests using domain knowledge, correlation structure, and causal or pipeline relationships to decide which features belong together. If the grouping is too coarse, you can overstate the importance of a bundle; if it is too narrow, you can still understate shared signal.
What practitioners should check before trusting permutation scores
Permutation importance is most reliable when features are relatively independent and the model does not rely on duplicated signals. Once collinearity is present, the score should be treated as a local estimate of marginal contribution, not a full accounting of predictive value. The practical question is whether you are trying to rank standalone features or measure the value of a correlated signal cluster.
When results look surprising, inspect the correlation structure and compare single-feature permutation with grouped permutation or alternative attribution methods. If a feature only looks weak because its correlated partner is still present, that is a signal to rethink the feature set, not to discard the variable automatically.
Practitioner takeaway: Permutation importance answers a marginal question, so correlated features can look weaker than their true shared contribution. If the features move together, test them together before making feature-selection or interpretation decisions.
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