Machine Learning Intermediate
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.
Plain English Explanation
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.
How is it used?
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.