Recently, I was exploring techniques to interpolate some missing environmental data, and stumbled across something called ‘random forest’ analysis. Random what now? I did a little digging and came across the massive and insanely complicated field of machine learning. I couldn’t find a concise guide to machine learning techniques, or when I might want to use one or the other, so I thought I would cobble together a brief guide on my own. Below is a rough stab at explaining and exploring different machine learning techniques, from CARTs to GBMs, using R.