Java Reference
In-Depth Information
Table 12-1
ROC Object—example data contents (continued)
Probability
Threshold
False Alarm
Hit Rate
True Neg.
False Neg.
True Pos.
False Pos.
0.35
0.945291
24150
639
11047
13004
0.075
0.4
0.960988
22293
455
11231
14862
0.071
0.45
0.973021
20435
315
11371
16719
0.039
0.5
0.982192
18577
208
11478
18577
0.034
0.55
0.988682
16719
132
11554
20435
0.012
0.6
0.992834
14862
83
11603
22293
0.007
0.65
0.995267
13004
55
11631
24150
0.006
0.7
0.997004
11146
35
11651
26008
0.004
0.75
0.998207
9288
20
11666
27866
0.003
0.8
0.998736
7431
14
11672
29724
0.003
0.85
0.999135
5573
10
11676
31581
0.002
0.9
0.999436
3715
6
11680
33439
0.002
0.95
0.9997
1857
3
11683
35297
0.001
1.0
1.0
0
0
11687
37155
0
The data points contained in this table are often depicted as the
ROC curve gains chart, which shows the true positive rate, or hit
rate , against the false positive rate, or false alarm rate , as shown in
Figure 12-2. For each point on this curve, JDM also provides all ele-
ments of the confusion matrix associated with the probability thresh-
old. In other words, the third row of Table 12-1 tells you that, if you
select all the customers with a probability higher than 0.36 (value of
the last column), it will return 7,882
3,715 “positive” customers
(the sum of the true positive and false positive cases). Here, remem-
ber that positive/negative means selected/not selected by the model,
and true/false means correctly/incorrectly classified. Table 12-1's
first row entry means that the probability threshold is so high that no
customers are selected. In the last row entry, the threshold is so low
that all customers are selected. Now, to come back to the third row,
7,882 customers were correctly classified, which means that, in our
scenario, they are buying the product proposed by HEW, and 3,715
were contacted but did not buy the product. The nice thing about all
Search WWH ::

Custom Search