**What is Variable Importance?**

It is the relative importance of each variable in your model’s prediction. The most impactful variable gets a value of 100 and then each subsequent variable is relative to the top one. If the second variable has a value of 88 then that variable is 88% as important as the top variable or you could say 12% less important.

**How is Variable Importance computed?**

It is dependent on the algorithm, and is generally related to how the algorithm’s approach estimates its importance. E.g., random forest evaluates along the lines of the percentage of trees a variable appears in along with how important it is in the tree. CART looks at Gini or similar score values. Linear regression models are based mainly on statistical significance levels.