Random Forest

In the middle of the accuracy-interpretability spectrum are random forests. We’ve often seen them described as “black boxes,” which we think this is unfair — maybe “gray” but certainly not “black”!

Random forests are collections of decision trees, like the one drawn below. The splits in each tree are chosen from random subsets of our features, so the trees all look slightly different. A single tree can be easily interpreted, assuming it is not grown too deep. But how we can interpret a random forest that contains hundreds or thousands of trees?