An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 36

• The definition of the target event and the time periods used in this example
are purely indicative. A different time frame for the historical or latency period
could be used according to the specific task and business situation.
FINDING USEFUL PREDICTORS WITH SUPERVISED FIELD SCREENING
MODELS
Another class of supervised modeling techniques includes the supervised field
screening models (Figure 2.8). These are models that usually serve as a preparation
step for the development of classification and estimation models. The situation of
having hundreds or even thousands of candidate predictors is not an unusual one
in complicated data mining tasks. Some of these fields, though, may not have an
influence on the output field that we want to predict. The role of supervised field
screening models is to assess all the available inputs and find the key predictors and
those predictors with marginal or no importance that are candidates for potential
removal from the predictive model.
Some predictive algorithms, including decision trees, for example, integrate
screening mechanisms that internally filter out the unrelated predictors. There
are some other algorithms which are inefficient when handling a large number
of candidate predictors at reasonable times. The field screening models can
efficiently reduce data dimensionality, retaining only those fields relevant to the
Figure 2.8 Supervised field screening models.