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A measure of variable importance was estimated by taking a scaled composite of the importance scores outputted by random forests.
Importance was estimated by permuting the values for each predictor variable and assessing the impact of the permutation on the model error.
A medium importance was estimated for EU decision makers and transport industry managers (see Table 4, Annex); fundamental structural changes of economy and production appear to be out of scope.
The relative prognostic importance was estimated, and the influence of other predictors was assessed by means of a Cox's proportional regression model.
The maximum Gini Index over all the genes in every permutation was recorded and thereby an empirical distribution of the maximum importance was estimated, as in similar analyses [ 49, 50].
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The average variable importance (AVI) was estimated.
For 13 major crops of global importance, it was estimated that 46 wild species have been used in released cultivars, and that furthermore, the introgression breeding approach is increasing [ 4].
The relative importance of predictors was estimated by standardized regression coefficients of sparse learning.
The importance of each predictor was estimated for the ensemble model prediction using the "variables_importance" function available in biomod2 (Thuiller et al. 2015).
The relative importance of each model was estimated through ranking the models by ΔAICc = AICci-AICcmin (where AICcmin is the best model in the model subset).
The relative importance of each variable was estimated using the jack-knife method.
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CEO of Professional Science Editing for Scientists @ prosciediting.com