With Attrition Model A
With Attrition Model B
Without Model (Random)
Lift charts: (a) Cumulative lift, (b) cumulative gain.
Following are the typical high-level steps used to compute cumula-
tive lift and gain values:
• Compute the positive target value probability for all test
• Sort cases in descending order of the positive target value
• Split the sorted test dataset cases into n groups, also known
as quantiles .
• Compute lift for each quantile—the ratio of the cumulative
number of positive targets and cumulative number of posi-
tive targets that can be found at random.
• Compute the cumulative gain for each quantile—the ratio
of cumulative predicted number of positive targets using
the model and total number of positive targets in the test
Table 7-5 details lift and cumulative gain calculations for our cus-
tomer dataset example. Each row of this table represents a quantile
that has 100 customer cases. In the test dataset, there are 1,000 cases,
of which 190 are known to be attriters. Hence, picking a random
customer from this dataset, we have a 19 percent probability that the
customer is an attriter.