What does z-score mean in Boruta The boruta algorithm performs the following steps. (here). Can anyone explain me what exactly the z-score means in this context?
I am referring to point 5 and 6 in the following list. I only know the general formula of the z-score: $z = \frac{x-\mu}{\sigma}$

1.Create duplicate copies of all independent variables. When the number of independent variables in the original data is less than 5, create at least 5 copies using existing variables.
2.Shuffle the values of added duplicate copies to remove their correlations with the target variable. It is called shadow features or permuted copies.
3.Combine the original ones with shuffled copies
4.Run a random forest classifier on the combined dataset and performs a variable importance measure (the default is Mean Decrease Accuracy) to evaluate the importance of each variable where higher means more important.
5.Then Z score is computed. It means mean of accuracy loss divided by standard deviation of accuracy loss.
6.Find the maximum Z score among shadow attributes (MZSA)
7.Tag the variables as 'unimportant'  when they have importance significantly lower than MZSA. Then we permanently remove them from the process.
8.Tag the variables as 'important'  when they have importance significantly higher than MZSA.
9.Repeat the above steps for predefined number of iterations (random forest runs), or until all attributes are either tagged 'unimportant' or 'important', whichever comes first.

 A: This is described in the second chapter of the Feature Selection with the Boruta Package paper by Kursa and Rudnicki:

Boruta algorithm is a wrapper built around the random forest
  classification algorithm [...] It is an
  ensemble method in which classification is performed by voting of
  multiple unbiased weak classifiers — decision trees. These trees are
  independently developed on different bagging samples of the training
  set. The importance measure of an attribute is obtained as the loss of
  accuracy of classification caused by the random permutation of
  attribute values between objects. It is computed separately for all
  trees in the forest which use a given attribute for classification.
  Then the average and standard deviation of the accuracy loss are
  computed. Alternatively, the $Z$ score computed by dividing the
  average loss by its standard deviation can be used as the importance
  measure. Unfortunately the $Z$ score is not directly related to the
  statistical significance of the feature importance returned by the
  random forest algorithm, since its distribution is not $N(0, 1)$
  (Rudnicki, Kierczak, Koronacki, and Komorowski 2006). Nevertheless, in
  Boruta we use $Z$ score as the importance measure since it takes into
  account the fluctuations of the mean accuracy loss among trees in the
  forest.

