Fundamental concepts: Identifying informative attributes; Segmenting data by progressive attribute selection.
Exemplary techniques: Finding correlations; Attribute/variable selection; Tree induction.
The previous chapters discussed models and modeling at a high level. This chapter delves into one of the main topics of data mining: predictive modeling. Following our example of data mining for churn prediction from the first section, we will begin by thinking of predictive modeling as supervised segmentation—how can we segment the population into groups that differ from each other with respect to some quantity of interest. In particular, how can we segment the population with respect to something that we would like to predict or estimate. The target of this prediction can be something we would like to avoid, such as which customers are likely to leave the company when their contracts expire, which accounts have been defrauded, which potential customers are likely not to pay off their account balances (write-offs, such as defaulting on one’s phone bill or credit card balance), or which web pages contain objectionable content. The target might instead be cast in a positive light, such as which consumers are most likely to respond to an advertisement or special offer, or which web pages are most appropriate for a search query.
In the process of discussing supervised segmentation, we introduce one ...