Permuted variable importance for a random forest. — permuted

For each feature, measures the drop in OOB accuracy (classification) or the increase in normalised MSE (regression) after randomly permuting that feature across the OOB observations. Computed lazily from the training data stored on the model; the result is cached.

Usage

permuted_importance(model)

Arguments

model: A pprf forest model.

Value

A numeric vector, one entry per feature. Negative values are meaningful (see Sign semantics above).

Details

**Sign semantics.** Entries may be **negative**. That is not an error and not a sentinel: it means permuting the feature did not degrade OOB fit on average — the feature's signal sits at or below the noise floor of the permutation procedure. Interpret negative or near-zero entries as "no evidence of importance"; rely on the ranking rather than clipping at zero or normalizing. The scale is already comparable within a fitted model.