While bagging improves the prediction accuracy of decision trees, it reduces model interpretability because the ensemble can no longer be represented as a single tree. Variable importance measures address this by calculating the significance of predictors across all $B$ bagged trees. For regression trees, a variable's importance is determined by the total amount the Residual Sum of Squares (RSS) is decreased due to splits over that predictor, averaged over all $B$ trees. For classification trees, importance is measured by the total decrease in the Gini index caused by splits over the specific predictor, averaged across all $B$ trees. A larger average decrease indicates a more important predictor.