How to interpret the important features identified in multivariate ROC exploratory analysis?

In MetaboAnalyst Biomarker Analysis module, important features are selected from the training data at each cross-validation (CV) run.

The orders and identities of the list could be (hopefully slight) different each time. This is because in each CV, important features will be selected based on the different samples!

There are two forms in reporting the most important features. The Frequency of being selected shows the stability of the rank of the importance for a given biomarker, and the Average importance measure provides a quantitative measure of the importance for a given biomarker. The first measure is more robust and not sensitive to outliers. In most cases, these two approaches should produce exactly the same list.

An example output (based on frequency) is shown below