I am trying to understand - when would someone choose Random Forest over XGBoost and vice versa. All the articles out there highlights on the differences between both. I understand them. But when actually given a real world data set, how should we approach the problem to choose between these?
For eg: Is there a set of statistical tests for variance check, and then choose? Or is it simply like you have a number of features, and cannot really choose to do parameter tuning, so you apply Random Forest to get results?