There was a time when the go to machine learning library was Weka, a behemoth of a Java library. Recently, Scikit-Learn has chipped away at the functionality provided by Weka, and given the Python community a comparable machine learning all-in-one library. In this talk Brian will discuss how Scikit-Learn is used to solve organic & inorganic problems at bitly. An organic decode was one in which a user makes an explicit decision to click on a link, and inorganic decode is one in which a link gets triggered without the users explicit knowledge. An example of an inorganic type of link is using a bitly encoded link to wrap a small gif that is embedded in a web page. Often these links get over inflated decode counts, which gives the naive appearance of them being popular. Brian will show how Scikit-Learn is used to decide on discriminative features, build the classifier, and test the classifier.
This talk was presented at PyData NYC 2012: nyc2012.pydata.org/. If you are interested in this topic, be sure to check out PyData Silicon Valley in March of 2013: sv2013.pydata.org/