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The interpretation of the relative importance method is also quite satisfactory in the ANN.
This paper uses the relative importance method of the input variables, as given by the Garson equation (1991).
For instance, for the results reported in "Illustration: implementing the proposed schema on the 'DITCH' agent-based model" section, we performed multiple sensitivity analysis techniques such variable importance method, recursive elimination method, and PRCC (partial rank correlation coefficient).
Moreover, the variable importance method based on a locally weighted regression model is applied to determine the key variables influencing both aleatory and epistemic uncertainty of energy use for this retail building.
This was achieved by feature selection approach using variable importance method in random forest.
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Similarly, the minimum reduction is 1134 470 variables and 1132 432 variables by scaled and unscaled importance methods respectively.
Among several predictor importance methods currently available, dominance analysis has been identified as one of the most useful approaches.
We investigate the parameter dependence of our importance sampling method and we validate it by comparison to an existing method.
The abovementioned methods are of significant reference value for our node importance evaluation method.
The Orange methods available for global ranking of features have been extended by the Random Trees (RT) variable importance assessment method [19] in OpenCV.
Convergence analysis of the importance sampling method was performed.
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