Which of the following is more critical about the model?

Principles Of Marketing
17th Edition
ISBN:9780134492513
Author:Kotler, Philip, Armstrong, Gary (gary M.)
Publisher:Kotler, Philip, Armstrong, Gary (gary M.)
Chapter1: Marketing: Creating Customer Value And Engagement
Section: Chapter Questions
Problem 1.1DQ
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MULTIPLE CHOICE QUESTIONS
An ecommerce company decides to send a gift voucher worth $1000 to a set of customers they believe would make a
purchase of at least $5000. They develop a model to identify potential customers who can make a purchase of $5000 if
we send the $1000 voucher. Which of the following is more critical about the model?
O False negative is worse than False positive if voucher amount crosses $5000
O Both False positive and negative are acceptable
O False positive is worse than a False negative
O False negative is worse than a False positive
Which of the following statements is TRUE?
O Tree based models are normally not affected by outliers
O Presence of outliers in the data has no effect on Regression models
O Standard scaler performs well on features that have outliers
O Tree-based models do not rely on dividing the feature space into sub-sections
Transcribed Image Text:MULTIPLE CHOICE QUESTIONS An ecommerce company decides to send a gift voucher worth $1000 to a set of customers they believe would make a purchase of at least $5000. They develop a model to identify potential customers who can make a purchase of $5000 if we send the $1000 voucher. Which of the following is more critical about the model? O False negative is worse than False positive if voucher amount crosses $5000 O Both False positive and negative are acceptable O False positive is worse than a False negative O False negative is worse than a False positive Which of the following statements is TRUE? O Tree based models are normally not affected by outliers O Presence of outliers in the data has no effect on Regression models O Standard scaler performs well on features that have outliers O Tree-based models do not rely on dividing the feature space into sub-sections
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