Permutation importance is a technique used to measure the significance of individual features in a predictive model by assessing the increase in prediction error when the values of a feature are randomly shuffled. This method provides insights into which features are most impactful for the model's performance, thus helping in understanding the underlying data and refining the model. In the context of supervised learning, it helps in evaluating feature contributions after a model has been trained, making it easier to interpret the results.
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