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Table 2 describes the physicians' approach to diagnosing patients with chest pain by registering the selected importance of different aspects of the diagnostic process.
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Predictor variables essential to accurate land cover classification were selected using importance indices statistically derived with classification trees.
The determinants for the performance objectives in this study were based on literature review and focus group interviews and were selected on importance and changeability for the specific target group.
As a measurement of importance of the 909 selected probes, variable importance of projection (VIP) values were calculated using the R library plsVarSel.
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.
By carefully selecting the importance sampling distribution, however, we can obtain a significant saving in the overall complexity.
The implication of the results for the codified designs and for selecting the importance factor is discussed.
Best predictions were obtained when vis NIR and MIR were combined and important spectral features were selected through variable importance projection (VIP) approach.
Biophysical variables such as the percent developed imperviousness and NDVI were selected with high importance, but other biophysical variables explaining building heights showed relatively lower variable importance than other two variables.
Following Mayen et al. ([2007]), the relative importance of each selected attribute over total importance were calculated (Table 6).
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