Mean decrease impurity is a metric used to measure the importance of a feature in decision trees and ensemble methods like random forests. It quantifies how much each feature contributes to reducing uncertainty or impurity in the dataset when making predictions. This metric helps identify which features are most influential in creating accurate models, providing insight into data-driven decision-making.
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