Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner
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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Chapter 5, Problem 4P

Consider Figure 5.16, the decile-wise lift chart for the transaction data model, applied to new data.

Chapter 5, Problem 4P, Consider Figure 5.16, the decile-wise lift chart for the transaction data model, applied to new

a. Interpret the meaning of the first and second bars from the left.

b. Explain how you might use this information in practise.

c. Another analyst comments that you could improve the accuracy of the model by classifying everything as nonfraudulent. If you do that, what is the error rate?

d. Comment on the usefulness, in this situation, of these two metrics of model performance (error rate and lift).

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We need to first make some adjustments to certain variables in Tableau and better understand the difference between Dimensions and Measures. Dimensions (shown as blue variable names) are normally attributes about certain transactions that can't be summed or otherwise quantified (e.g., dates, product numbers, invoice numbers, etc.), Measures (shown in green variable names), on the other hand, are normally the outcome variables we are most interested in (e.g., invoice total, quantity shipped, etc.). Sometimes, Tableau misclassifies these variables and we need to make changes before we can continue. In the shipping file, Tableau has classified Customer No. and Invoice No. as Measures instead of Dimensions. We need to set both of these as dimensions. • Right-click on Customer Number and press "Convert to Dimension." • Repeat the previous step for Invoice Number. What other "number" has been imported as a Measure instead of a Dimension? Please change it to a Measure Dimension using the…
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Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner

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