An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 18

According to the measurement level of the field to be predicted, supervised
models are further categorized into:
• Classification or propensity modeling techniques.
• Estimation or regression modeling techniques.
A categorical or symbolic field contains discrete values which denote mem-
bership of known groups or predefined classes. A categorical field may be a flag
(dichotomous or binary) field with Yes/No or True/False values or a set field with
more than two outcomes. Typical examples of categorical fields and outcomes
include:
• Accepted a marketing offer. [Yes/No]
• Good credit risk/bad credit risk.
• Churned/stayed active.
These outcomes are associated with the occurrence of specific events.
When the target is categorical, the use of a classification model is appropri-
ate. These models analyze discrete outcomes and are used to classify new
records into the predefined classes. In other words, they predict events. Con-
fidence scores supplement their predictions, denoting the likelihood of a particular
outcome.
On the other hand, there are fields with continuous numeric values (range
values), such as:
• The balance of bank accounts
• The amount of credit card purchases of each card holder
• The number of total telecommunication calls made by each customer.
In such cases, when analysts want to estimate continuous outcome values,
estimationmodels are applied. Thesemodels are also referred to as regressionmod-
els after the respective statistical technique. Nowadays, though, other estimation
techniques are also available.
Another use of supervised models is in the screening of predictors. These
models are used as a preparatory step before the development of a predictive
model. They assess the predictive importance of the original input fields and
identify the significant predictors. Predictors with little or no predictive power are
removed from the subsequent modeling steps.
The different uses of supervised modeling techniques are depicted in
Figure 2.1.