Let us say there are two attrition models, Model A and Model B ,
that are built using different algorithms or with different settings
using the same algorithm. Figure 7-8 illustrates the cumulative lift
and cumulative gains charts plotted for these models to compare the
results. Note that Model A outperforms Model B in the first two
quantiles; however, Model B outperforms from the third quantile
onward. A user can pick Model A when the budget allows for at
most 20 percent of customers, otherwise the user can pick Model B if
more than 20 percent of customers are budgeted.
Apply Model: Obtain Prediction Results
After evaluating model performance using the test data, the user
selects the best model for the problem and applies it to predict target
values for an apply dataset. As noted for decision tree, some algorithms
may use a subset of the input attributes in the final model. This sub-
set of attributes is called the model signature , and it can be retrieved
from the model to determine which attributes are required to apply
In this section, we take the simple decision tree model discussed
in Section 7.1.5 to illustrate the model apply operation. This model
has three input attributes: age, capital gain, and average savings balance
as shown in Table 7-3 and the model uses only two of these, age and
average savings balance , as shown in Figure 7-9. These two attributes
form the model signature. Consequently, to use this model, the
apply dataset for this model needs only contain cases with age and
average savings balance attribute values. Consider an apply dataset
that has two customer cases for customers Jones and Smith as shown
in Table 7-6 to understand the apply process.
Figure 7-9 illustrates how the decision tree model predicts if these
customers are attriters. Jones is older than 36, so from the root node
he is assigned to node-2 that predicts him as an attriter . Smith is
younger than 36, so he is assigned to node-3 and node-3 further splits
Customer apply table
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