Permutation Importance
Permutation Importance is a method used to determine the importance of features in a machine learning model. It works by randomly shuffling the values of a feature and measuring the resulting decrease in the model's performance. The greater the decrease in performance, the more important the feature is considered to be. This method is useful for identifying which features have the greatest impact on a model's predictions and can be used for feature selection and model interpretation.
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