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The feature importance is calculated by using a Random Forest based algorithm, Boruta.
In the DT algorithm, the estimation of feature importance is calculated from the feature usage based on information gain.
Descriptor importance is calculated in the frame of Bayesian Automatic Relevance Determination (ARD) as the inverse of the value of the length scale of each descriptor.
The level of importance is calculated based on the number of HOs executed between the faulty cell and the other cells of the network.
The relative importance is calculated as the sum of the Akaike weights of the models containing each variable.
The relative importance is calculated as the difference between these prediction accuracies (that of the original OOB and that of the OOB with the permuted variable).
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Following Mayen et al. ([2007]), the relative importance of each selected attribute over total importance were calculated (Table 6).
For biomod2, the relative importance was calculated for each predictor, indicating its importance in explaining the distribution of Hakea sericea in the study area (for more information see Appendix III) The predictor with the highest importance explaining the local-scale distribution of H. sericea was the percentage of foliated metamorphic rocks, or schists (MetRock = 0.613; Table 2).
Allele frequencies and the following statistical parameters of forensic importance were calculated using the Excel spreadsheet Powerstats (Tereba 2001): allele frequencies, minimum allele frequencies (MAF), probability of exclusion (PE), power of discrimination (PD), polymorphism information content (PIC), observed heterozygosity (Het), and typical paternity index (TPI).
The mean and standard deviation (S.D). of the model performance (R2) and variable importance were calculated to evaluate the model sensitivity.
Additionally, the percentages of patients grading each item as of no importance, little importance, and great importance were calculated.
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