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For Visitors From GigaOM, A Little More On Overkill Analytics

If you’re here, you’ve probably already read this, but GigaOM‘s Derrick Harris wrote this great article discussing their WordPress Challenge and the fledgling overkill analytics approach I used to create the winning entry.  

Forgive me for the self-indulgence, but here’s a quick excerpt:

And therein lies the beauty of overkill analytics, a term that Carter might have coined, but that appears to be catching on — especially in the world of web companies and big data. Carter says he doesn’t want to spend a lot of time fine-tuning models, writing complex algorithms or pre-analyzing data to make it work for his purposes. Rather, he wants to utilize some simple models, reduce things to numbers and process the heck out of the data set on as much hardware as is possible.

I’m pleased GigaOM found the overkill analytics characterization worth discussing. It’s brought a host of visitors to the site (at least compared to the readership I thought I’d have).

With the additional visits, though, I feel I should put a little more meat on the bones about my development philosophy. I’m sure this will be familiar territory for seasoned data scientists, but below are four principles that guide my approach:

  • Spend most of your time engineering mass quantities of features. Synthesizing raw data about the subject into pertinent, lower-dimensional metrics always brings the biggest bang for your buck. More features with more diversity are always better, even if some are simplistic or unsophisticated.
  • Spend very little of your time comparing, selecting, and fine-tuning models. A simple ensemble of many crude models is usually better than a perfectly-calibrated ensemble of a more precise models.
  • Spend no time making your algorithm elegant, optimized, or theoretically sound. Use cheap servers and cheap tricks instead.
  • Get results first; explain them later. The statistical algorithms available are powerful and unbiased – they will find the key elements before you do. Your job is to feed them mass quantities of features and then explain and interpret what they find, not guide them to a preconceived intuition about the answer.

To an extent, this is just a restatement of some best practices in predictive modeling. However, where overkill analytics comes in is to take these principles to new and hopefully productive extremes by leveraging cheap cloud computing power and rapid development principles. It is in this aspect where I hope to offer some innovation and new approaches.

Anyway, thanks for the visits. I will publish a couple more posts next week on the WordPress Challenge, one describing and ranking the full set of features I used, and one on the power of simple ensembles to improve results on this type of real-world problem.

As always, thanks for reading.