Decision Trees and Random Forests are two powerful tree based machine learning algorithms which are predominantly used by Data scientists. One of the remarkable advantage of using tree based algorithm is that they can be easily interpreted. Also this makes it straight forward to derive the importance of each variable on the decision making process of tree based approach. In simple words, In tree based methods it is easy to compute how much each variable contributes to that decision. In this article, we will see two approaches for feature selection using tree based models.