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b. What is the lift value of the leftmost bar? Note: Round your answer to 2 decimal places. c. What is the area under the

image text in transcribedimage text in transcribedimage text in transcribedimage text in transcribedimage text in transcribedimage text in transcribedimage text in transcribed b. What is the lift value of the leftmost bar? Note: Round your answer to 2 decimal places. c. What is the area under the ROC curve (or AUC value)? Note: Round your answer to 4 decimal places. d. Which of the following statements is least accurate? A. By selecting the top 10% of the validation cases with the highest predicted probability of belonging to the target class, the nave Bayes model would identify more target class cases than if the cases are randomly selected. B. Using 0.5 as the cutoff rate, the naive Bayes model's accuracy rate is higher than that of the nave rule (classifying all cases to the predominant class) for the validation data. C. The lift curve of the nave Bayes model lies mostly above the lift curve of the baseline model. D. The nave Bayes model performs better than the baseline model in terms of both sensitivity and specificity across all For Analytic Solver, partition data sets into 60% training and 40% validation and use 12345 as the default random seed. If the predictor variable values are in the character format, then treat the predictor variable as a categorical variable. Otherwise, treat the predictor variable as a numerical variable. An online retailer is offering a new line of running shoes. The retailer plans to send out an e-mail with a discount offer to some of its existing customers and wants to know if it can use data mining analysis to predict whether or not a customer might respond to its email offer. The retailer prepares the accompanying data file of 170 existing customers who had received online promotions in the past, which includes the following variables: Purchase ( 1 if purchase, 0 otherwise); Age ( 1 for 20 years and younger, 2 for 21 to 30 years, 3 for 31 to 40 years, 4 for 41 to 50 years, and 5 for 51 and older); Income (1 for $0 to $50K,2 for $51K to $80K,3 for $81K to $100K,4 for $100K+ ); and PastPurchase (1 for no past purchase, 2 for 1 or 2 past purchases, 3 for 3 to 6 past purchases, 4 for 7 or more past purchases). a. Partition the data to develop a nave Bayes classification model. Report the accuracy, sensitivity, specificity, and precision rates for the validation data set. Note: Enter your answers as decimals and round them to 2 decimal places. b. What is the lift value of the leftmost bar

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