Classification is a classic data mining task, with roots in machine learning. A typical application is : "Given past records of customers who switched to another supplier, predict which current customers are likely to do the same." This specific application is known as Churn Prediction, but there are very many other applications such as predicting response to a direct marketing campaign, seperating good products from faulty ones etc.

The "Classification Problem" involves data which is divided into two or more groups, or classes. In our example above, the two classes are "switched supplier" and "didn't switch". The data mining software is asked to tell us which of the groups a new example falls into.

So, we might train the software using breast actives customer records from the last year, divided into our two groups. We then ask the software to predict which of our customers we're likely to lose.

Of course, to ensure we can trust the predictions, there is generally a testing or validation stage as well.

See also: Data Mining Software

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Topic revision: r7 - 16 May 2013 - 10:32:00 - Jack Uack

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