A SunBurst of Insight

This is a guest post by David Gerster (@gerster), a data scientist and investor in BigML.

I work at a consumer web company, and recently used BigML to understand what drives return visits to our site. I followed Standard Operating Procedure for data mining, sampling a group of users, dividing them into two classes, and creating several features that I hoped would be useful in predicting these classes. I then fed this training data to BigML, which quickly and obediently produced a decision tree:

Next I used BigML’s interface to examine the tree’s many subsets, shown as “nodes” in the diagram above. I moused over a node at the top of the tree and saw that it achieved high separation for a large fraction of the training set:

This one node covered 58% of the data, and separated the two classes with 73% confidence. (“Confidence” is a measure…