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The higher the value of node purity, the higher the importance of a variable is.
The importance of a variable in a PC model is indicated by the size of its residual variance.
Factor loadings used to determine the relative importance of a variable as compared to other variables in a PC were classified as 'strong'moderateand'weakd 'weak' corresponding to absolute loading value of >0.75, 0.50 0.75, and 0.30 0.50 respectively [44, 45].
To evaluate the importance of a variable in the RF algorithm, the difference in the OOB error/mean square error or the Gini index was calculated in each tree when the variable was randomly permuted while all of the other variables remained the same.
Relief evaluates the importance of a variable by repeatedly sampling an instance and checking the value of the given variable for the nearest instance from the same and different classes.
The local variable importance is an estimate of the importance of a variable for the classification of a single sample.
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Note that importance of a particular variable expresses how important it is relative to the most important variable (getting a score of 100). Figure 1 shows that the study population in which 83% 'ever used' condoms, was first partitioned between those with and without a perceived need for condoms (86% versus 14%).
Their study's variables closely resemble those examined in the present study and illustrate the importance of including a variable related to perceived value in any health insurance study.
For the multivariate models, a single linear trend variable across categories was used for assessing the importance of a new variable.
The variable importance measure of a variable can be explained as the contribution variables make to the construction of the tree.
A quantitative ranking of the variables (i.e. relative importance) was generated by CART®, based upon the frequency and importance of a given variable in the tree building process.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com