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In-Depth Information

5

100

90

4

80

70

3

60

50

40

2

30

20

1

10

0

0

0123456789 0

0123456789 0

Quantile Number

Quantile Number

(a)

(b)

With Attrition Model A

With Attrition Model B

Without Model (Random)

Figure 7-8

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

dataset cases.

•
Sort cases in descending order of the positive target value

probability.

•
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

dataset.

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.

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