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large number of candidate inputs. You can select variables that you want to exclude. You can also select a frequency
variable that represents a weight that you want to apply to the rows of data. You can select ID variables that are to be
excluded from the analysis. It is often helpful to identify these variables for reporting and score selection. Figure 4. SAS Rapid Predictive Modeler Data Panel
You specify the type of model you want to build in the Model panel. SAS Enterprise Miner 6.1 modeling functions are
executed when you run the SAS Rapid Predictive Modeler to generate each model. The basic model samples the
data only in the case where you have a rare target event and then partitions the data using the target as a
stratification variable. It then applies a one-level decision-tree-based variable selection step. The selected input
variables are then binned with respect to their relationship with the target and are passed to a forward stepwise
The intermediate model is an extension of the basic. Multiple transformations are provided to several variable
selection techniques. A decision tree, regression model, and a logistic regression (which contains the node variable
from a decision tree from a predecessor node) are used as modeling techniques. The node variable exported from a
decision tree is one way to represent interactions. The advanced model extends the intermediate methodology to
include a neural network model, advanced regression analysis, and ensemble models.
The basic model runs faster than the intermediate model but might create a less accurate model. The same is often
true as you progress from using the intermediate to the advanced model.
Figure 5 shows the Decisions and Priors window, where you set optional information about categorical target
variables. SAS Rapid Predictive Modeler automatically makes a pass only through the data for the target variable to
set the event level based on descending order and also to calculate the data proportion for each target level. The
model comparison statistics are dependent on the target event level which for this analysis is correctly set at 1. 5 SAS Global Forum 2010 Customer Intelligence Before you develop predictive models, it is important to specify the correct priors to properly adjust model predictions
regardless of what the proportions are in the training data. If no prior probabilities are used, the estimated posterior
probability for the churn=1 event class would be too high. In this example the training data have been oversampled to
include 49% churners (1’s) and 51% non-churners (0’s). However, it is known that the population historically contains
about 4% churners and 96% non-churners, so the priors are adjusted accordingly as shown in Figure 5.
You use the decision options to bias the model construction to a particular event. For example, if it is more
important to identify churners as churners than non-churners as non-churners, then...
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This note was uploaded on 09/30/2013 for the course FINANCE 4013 taught by Professor Jamescameron during the Summer '10 term at Ohio State.
- Summer '10