Using machine learning to develop your hypothesis

There are many applications for machine learning, but one of the most exciting is using it to create hypothesis to test. An article in the Economist, “Computer says ‘try this’,” discusses many of the ways computers are now creating hypothesis to move medicine, farming and even cooking forward.

There are already many projects using machine learning to generate valuable and novel hypotheses. One project, BrainSCANr, suggests research topics for neuro-scientists by looking at millions of peer-reviewed papers. Research published by Baylor College of Medicine researchers used machine-learning software to review over 150,000 papers on a technique to curb the growth of cancer (proteins called kinases). The algorithm led to seven new kinases that researchers had missed. The technique is even being used to analyze search terms in Bing and Internet Explorer to determine potentially dangerous pairings of medicine.

Machine learning generates strong hypotheses

What it means for you

While machine learning is already benefitting many tech and game companies, using it to help develop hypotheses for your business is invaluable. If you are a mobile game company, think of the value of having a machine-learning system suggest that rather than focusing on your premium exchange rate, you should test changing the frequency of your free-coin bonus. Or maybe it suggests that rather than testing your new content cadence you should test the efficacy of using IP. If you are an AirBnb, it may suggest that you test different rake options rather than how you sort options for users.

The power of machine learning for hypothesis testing is twofold:

  1. It directs your resources to where your testing will have the greatest impact on lifetime value. While your test may show statistically significant results, a different test may have a much bigger impact on metrics.
  2. It analyzes other people’s findings, from online articles to consumer behavior research, and uses this information to steer your tests. Rather than re-inventing the wheel, you can build on what other people have found.

Key takeaways

  1. A powerful potential application of machine learning is using it to determine the most important hypothesis for you to test.
  2. Machine learning can analyze different data points from your customers to suggest the most beneficial hypothesis to test.
  3. Machine learning can also synthesize research and other public information so you do not waste resources testing theories that have already been tested.
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