
…op in the error function for a variable at each split point gives us an idea of feature importance. It means that we record the total amount that the error is decreased due to splits over a given predictor, averaged over all bagged trees. A large value then indicates an important predictor. In regression problems this may be the drop in residual sum of squares and in classification this might be the Gini score.