The steps for calculating variable importance in Random Forest Algorithm are as follows:

**1.** For each tree grown in a random forest, find the number of votes for the correct class in out-of-bag data.

**2.** Now perform random permutation of a predictor’s values (let’s say variable-k) in the OOB data and then check the number of votes for the correct class. By “random permutation of a predictor’s values”, it means changing the order of values (shuffling).

**3.** At this step, we subtract the number of votes for the correct class in the variable-k-permuted data from the number of votes for the correct class in the original OOB data.

**4.** Now, the raw importance score for variable k is the average of this number over all trees in the forest. Then, we normalized the score by taking the standard deviation.

**5.** Variables having large values for this score are ranked as more important as building a current model without original values of a variable gives a worse prediction, which means the variable is important.