What is a permutation variable importances?
A type of variable importance that evaluates the increase in the prediction error of a model after permuting the feature's values.
permutation variable importances explained in plain English
A type of variable importance that evaluates the increase in the prediction error of a model after permuting the feature's values. Permutation variable importance is a model-independent metric.
Example
Practitioners refer to permutation variable importances when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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