An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 17

CHAPTER TWO

An Overview of Data Mining
Techniques

SUPERVISED MODELING

In supervised modeling, whether for the prediction of an event or for a continuous
numeric outcome, the availability of a training dataset with historical data is
required. Models learn from past cases. In order for predictive models to associate
input data patterns with specific outcomes, it is necessary to present them with
cases with known outcomes. This phase is called the training phase. During that
phase, the predictive algorithm builds the function that connects the inputs with
the target field. Once the relationships are identified and the model is evaluated
and proved to be of satisfactory predictive power, the scoring phase follows. New
records, for which the outcome values are unknown, are presented to the model
and scored accordingly.
Some predictive models such as regression and decision trees are transparent,
providing an explanation of their results. Besides prediction, these models can also
be used for insight and profiling. They can identify inputs with a significant effect
on the target attribute and they can reveal the type and magnitude of the effect.
For instance, supervised models can be applied to find the drivers associated with
customer satisfaction or attrition. Similarly, supervisedmodels can also supplement
traditional reporting techniques in the profiling of the segments of an organization
by identifying the differentiating features of each group.