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The method does not provide a statistical test for evaluating the assumption of differences between two importance variable scores, making it difficult to give a clear interpretation when the importance measures are close.
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VIMs, coupled with Pearson correlation, through various analyses indicated that total sulfur, liptinite, and vitrinite maximum reflectance (Rmax) are the most importance variables for the prediction of HGI.
The properties are of biological importance; variables that are affected by forces other than selection for mRNA structure – for example the amino acid sequence – should be fixed.
Using this importance, variables can be removed that do not significantly contribute to the classification or improve the prediction accuracy [ 26, 102, 103].
At the same time, we calculated Akaike weight (w) and the relative importance of variable (Burnham and Anderson 2002) for comparing the importance of each variable.
By employing m1ultivariate modelling and investigating additional experimental variables, the relative importance of variables effecting remediation was determined and response surfaces for heavy metal removal were calculated.
Since parity was considered of overall biological importance, this variable was forced into the models.
Owing to its importance, the variable "age" was included in the multivariable analysis.
RF analysis estimates the importance of variables in determining classification and is able to highlight possible variable interactions.
As we can see in Figure 4 results are very similar for the different importance of variables.
The biological importance of variables and their inverse density distributions were used to stratify and weight the grid cells [143].
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