What exactly is a "standard machine learning algorithm"?
I'm sure that probably means that they used something from scikit-learn but "comb[ing] through correlations" isn't as simple as clicking the Go button.
The rest of the article does start to get into labeling, holding out a test set, and some of the data cleanup (the real combing working).
I guess I was just hoping for more detail of how it worked and not that it worked. I get that this wasn't meant to be a PhD thesis on supervised machine learning, but the mechanics of data analysis are really interesting as a process of discovery.
Curious to know what others think. How did the balance of how vs that work for you?