Question
Evaluating Predictive Performance ( Business Analytics) Confusion Matrix !!! A publisher plans to boost the sales...
Evaluating Predictive Performance ( Business Analytics)
Confusion Matrix !!!
- A publisher plans to boost the sales of its most popular magazine by sending out promotional mails. We refer to a customer as a responder if he/she subscribes to the magazine for the next year after receiving a promotional mail. Otherwise the customer is referred to as a non-responder. Denote responder by C1 and non-responder by C2. The publisher has built a model to classify each customer as either a responder or a non-responder. In practice only 1% of the customers are responders, and the remaining 99% are non-responders. In order to build an unbiased model, the publisher employed the oversampling method in creating the training set and the validation set, such that both datasets contain 50% responders and 50% non-responders. Please adjust the following validation confusion matrix for oversampling, then compute accuracy measures using the adjusted confusion matrix. Keep at least three digits after the decimal point.
| Actual Class | |||
| C1 | C2 | ||
| Predicted Class | C1 | 645 | 112 |
| C2 | 255 | 788 | |
| Oversampling factor of C1: | |
| Oversampling factor of C2: |
Adjusted validation confusion matrix:
| Actual Class | |||
| C1 | C2 | ||
| Predicted Class | C1 | ||
| C2 | |||
| Overall Accuracy | |
| Overall Error Rate | |
| False Discovery Rate (FDR) | |
| False Omission Rate (FOR) | |
| Precision | |
| Specificity | |
| Sensitivity |
Answers
PAGE No. DATE Actual Class Given thot C i Tp 64S 452. Prredicted c class goo 645 + 88 64S" +チ8 8 + 112 t-2ss-- 019 61 1483 800 FP + FN 36子 180 0 o.2 O38 ③False Discovery Rate (FDR) FP 11 2 112 + 645-75구 112 0.1419 G False omission Rate CFoR) FN 2SJ -O 244PAGE No. DATE S Psecision Tp Tp+Fp 64S 64S" + 112" 6 衫子 6 7-88 788 TP TPHEN 64S 64S"十255-960 (7 64S 43166
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