Permutation importance is a technique used to estimate the contribution of individual features to the predictive performance of a machine learning model. This method involves shuffling the values of a feature and measuring the change in model performance, helping to determine how much that feature influences predictions. It's particularly useful in assessing feature significance in complex models like decision trees and random forests, where understanding individual contributions can be challenging.
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