Beginner's guide to machine learning in R (with step-by-step tutorial)

If you're a graduate of economics, psychology, sociology, medicine, biostatistics, ecology, or related fields, you probably have received some training in statistics, but much less likely in machine learning. This is a problem because machine-learning algorithms are much better capable to solve many real-world applications compared with the procedures we learned in statistics class (randomized experiments, significance tests, correlation, ANOVA, linear regression, and so on). In all of these examples, statistical models are used to solve the problem, but in a different way than how you learned it in "Introduction to Statistics". In this post I want to give you a brief introduction what "machine learning" means, what the differences to "classical" statistical procedures are, and how you can train a machine learning model in R for your own use case in 8 simple steps. Think of a facial-recognition app. How does the app know whether it's John or rather Jane it's looking at? A conventional approach would be: Create an exhaustive list of features about John which can be quantitatively measured for the computer to memorize. E.g.: Look for short, brown hair, a three-day beard, a prominent nose, a scar on the left forehead, the distance between his eyes is 10.4 centimeters, he often wears a black hat, etc., that's John. The machine-learning approach works differently: You feed a computer many pictures labelled "John" or "Jane", and that's it, you don't provide any additional information – rather, you let the machine infer the important features which best discern John from Jane. It might be that the form of the cheek bones are actually a better predictor of whether or not it's John on the image, rather than the hair color or the distance between the eyes. You don't care, you let the machine figure it out. Thus, this is a data-driven (inductive) approach, where a machine *learns* the rules how to classify faces (e.g., if X1 and X2 are present, then it's likely John) from a set of training data. You don't specify these rules manually. This is why machine learning is considered (a subfield of) artificial intelligence: The machine carries out tasks without being explicitly told what to do.