Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
3rd Edition
ISBN: 9781118729274
Author: Galit Shmueli, Peter C. Bruce, Nitin R. Patel
Publisher: WILEY
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Expert Solution & Answer
Chapter 5, Problem 5P
a.
Explanation of Solution
Given: The insurance records are evaluated to build the predicting model for the fraudulent claims. Only 1% is considered as fraudulent on the basis of the historic data.
The sample, n, which is applied, is 800. It classifies the values 310 and 270 as frauds and non-frauds, respectively. It misses 90 as frauds, where 130 records are found incorrect which are marked as fraud...
b.
Explanation of Solution
Given: The insurance records are evaluated to build the predicting model for the fraudulent claims. Only 1% is considered as fraudulent on the basis of the historic data.
The sample, n, which is applied, is 800. It classifies the values 310 and 270 as frauds and non-frauds, respectively. It misses 90 as frauds, where 130 records are found incorrect which are marked as fraud.
To find:Â The adjusted misclassification from the record of the predicating model.
Solution:
By analyzing the records from the classification matrix,
Predicted records of fraudulent without any record of non-fraudulent=310-90=220...
c.
Explanation of Solution
Given: The insurance records are evaluated to build the predicting model for the fraudulent claims. Only 1% is considered as fraudulent on the basis of the historic data.
The sample, n, which is applied, is 800. It classifies the values 310 and 270 as frauds and non-frauds, respectively. It misses 90 as frauds, where 130 records are found incorrect which are marked as fraud...
Expert Solution & Answer
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Model 1: train MSE = 0.423, test MSE = 0.978
Model 2: train MSE = 0.572, test MSE = 0.644
Model 3: train MSE = 0.218, test MSE = 1.103
Based on this information, which of these models generalises the best to unseen data?
The advantages of transitioning from stepwise regression to all-subsets regression are described in detail below.
Chapter 5 Solutions
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
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