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Table containing the 89 most important descriptors selected using variable importance calculated by random forests.
The 89 most important descriptors selected using variable importance calculated by random forests were individually analyzed.
The most important descriptors selected by the three methods are very different between each other, however the descriptor average molecular weight (AMW) appears as important to both genetic algorithms and variable importance calculated by random forests.
In terms of stability, genetic algorithms are not coherent on the set of features selected since, in general, only 2 or 3 variables are common per run while using variable importance calculated by random forests the list of most important descriptor is coherent.
13321_2012_374_MOESM1_ESM.xlsx Additional file 1: List of descriptors selected using different selection/reduction methods: principal components analysis, genetic algorithms and variable importance calculated by random forests.
Genetic algorithms select mostly Daylight fingerprints, while variable importance calculated by random forests give more importance to the 2D and 3D descriptors calculated by E-DRAGON.
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Additionally, the percentages of patients grading each item as of no importance, little importance, and great importance were calculated.
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).
Following Mayen et al. ([2007]), the relative importance of each selected attribute over total importance were calculated (Table 6).
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.
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