Enlarge / Turning the lens on ourselves, as it were.
There's a moment in any foray into new technological territory that you realize you may have embarked on a Sisyphean task. Staring at the multitude of options available to take on the project, you research your options, read the documentation, and start to work—only to find that actually just defining the problem may be more work than finding the actual solution.
Reader, this is where I found myself two weeks into this adventure in machine learning. I familiarized myself with the data, the tools, and the known approaches to problems with this kind of data, and I tried several approaches to solving what on the surface seemed to be a simple machine learning problem: Based on past performance, could we predict whether any given Ars headline will be a winner in an A/B test?
Things have not been going particularly well. In fact, as I finished this piece, my most recent attempt showed that our algorithm was about as accurate as a coin flip.
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