Gradient Boosting vs Random Forest – Abolfazl Ravanshad – Medium

In this post, I am going to compare two popular ensemble methods, Random Forests (RM) and Gradient Boosting Machine (GBM). GBM and RF both are ensemble learning methods and predict (regression or classification) by combining the outputs from individual trees (we assume tree-based GBM or GBT). They have all the strengths and weaknesses of the ensemble methods mentioned in my previous post. So, here we compare them only with respect to each other. GBM and RF differ in the way the trees are built: the order and the way the results are combined.