Data Science Concepts and Practice 2nd Edition by Unknown
Author:Unknown
Language: eng
Format: epub
8.4 How to Implement
271
Table 8.4 Scoring Predictions and Sorting by Confidences Is the Basis for Generating Lift Curves
Confidence of
Cumulative
Cumulative
Actual Class Predicted Class “Response”
Type? TP
FP
Quartile Gain
Lift
Response
Response
0.902
TP
1
0
1st
67%
2.666667
Response
Response
0.896
TP
2
0
1st
Response
Response
0.834
TP
3
0
1st
Response
Response
0.741
TP
4
0
1st
No response
Response
0.686
FP
4
1
1st
Response
Response
0.616
TP
5
1
2nd
100%
2
Response
Response
0.609
TP
6
1
2nd
No response
Response
0.576
FP
6
2
2nd
No response
Response
0.542
FP
6
3
2nd
No response
Response
0.530
FP
6
4
2nd
No response
No response
0.440
TN
6
4
3rd
100%
1.333333
No response
No response
0.428
TN
6
4
3rd
No response
No response
0.393
TN
6
4
3rd
No response
No response
0.313
TN
6
4
3rd
No response
No response
0.298
TN
6
4
3rd
No response
No response
0.260
TN
6
4
4th
100%
1
No response
No response
0.248
TN
6
4
4th
No response
No response
0.247
TN
6
4
4th
No response
No response
0.241
TN
6
4
4th
No response
No response
0.116
TN
6
4
4th
TP, true positive; FP, false positive; TN, true negative.
8.4 HOW TO IMPLEMENT
A built-in dataset in RapidMiner will be used to demonstrate how all the
three classification performances (confusion matrix, ROC/AUC, and lift/
gain charts) are evaluated. The process shown in Fig. 8.3 uses the Generate
Direct Mailing Data operator to create a 10,000 record dataset. The objective
of the modeling (Naïve Bayes used here) is to predict whether a person is
likely to respond to a direct mailing campaign or not based on demo-
graphic attributes (age, lifestyle, earnings, type of car, family status, and
sports affinity).
Step 1: Data Preparation
Create a dataset with 10,000 examples using the Generate Direct Mailing Data
operator by setting a local random seed (default 5 1992) to ensure
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