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.

Nora Jackson owns a number of vacation homes on a beach. She works with a consortium of rental home owners to gather a data set to build a classification model to predict the likelihood of potential customers renting a beachfront home during holidays. The accompanying data file includes the following variables: whether the potential customer owns a home (Own = 1 if yes, 0 otherwise), whether the customer has children (Children = 1 if yes, 0 otherwise), the customer's age in years (Age), annual income (Income), and whether or not the customer has previously rented a beachfront house (Rental = 1 if yes, 0 otherwise).

Click here for the Excel Data File

a. Bin the Age and Income variables. Choose the Equal count option and two bins for each of the two variables. What are the bin numbers for the Age and Income variables of the first two observations?

b-1. Partition the transformed data to develop a nave Bayes classification model. Use 0.5 as the cutoff value for the analysis. Report the accuracy, specificity, sensitivity, and precision rates (in proportions) for the validation data set.

Note: Enter your answers as decimals and round them to 2 decimal places.

b-2. Which of the following statements is most accurate?

multiple choice 1

• Using 0.5 as the cutoff value, the misclassification rate of the nave Bayes model is about 31%.
• The nave Bayes model is 64% more accurate than the nave rule (classifying all cases into the predominant class).
• Using 0.5 as the cutoff value, the nave Bayes model is able to correctly classify 81% of the customers who rent a beachfront house.
• The first 50% of the customers selected by the nave Bayes model have all rented a beachfront house.

c-1. Generate the cumulative lift chart. Is the following statement a true statement?

The cumulative lift chart shows that the nave Bayes model can identify about 105 customers who rent beachfront houses by selecting 200 of the potential customers with the highest predicted probabilities of renting a beachfront house.

multiple choice 2

• True
• False

c-2. Generate the decile-wise chart. What is the lift of the leftmost bar of the decile-wise chart?

Note: Round your answer to 2 decimal places.

d. Generate the ROC curve. What is the AUC value of the ROC curve?

Note: Round your answer to 4 decimal places.

e. Which of the following statements is least accurate?

multiple choice 3

• The ROC curve suggests that the nave Bayes model performs better than the baseline model in terms of sensitivity and specificity across all possible cutoff values.
• The cutoff value 0.5 is effective in identifying customers who would rent a beachfront house.
• The first 10% of the customers selected by the nave Bayes model include more individuals who would rent a beachfront house as compared to a randomly selected 10% of potential customers.
• The nave Bayes model is somewhat effective in predicting the likelihood of potential customer renting a beachfront home according to the following criterion: Effective if AUC 0.8; Somewhat effective if 0.5 < AUC < 0.8; Ineffective if AUC 0.5.

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.

Nora Jackson owns a number of vacation homes on a beach. She works with a consortium of rental home owners to gather a data set to build a classification model to predict the likelihood of potential customers renting a beachfront home during holidays. The accompanying data file includes the following variables: whether the potential customer owns a home (Own = 1 if yes, 0 otherwise), whether the customer has children (Children = 1 if yes, 0 otherwise), the customer's age in years (Age), annual income (Income), and whether or not the customer has previously rented a beachfront house (Rental = 1 if yes, 0 otherwise).

Click here for the Excel Data File

a. Bin the Age and Income variables. Choose the Equal count option and two bins for each of the two variables. What are the bin numbers for the Age and Income variables of the first two observations?

b-1. Partition the transformed data to develop a nave Bayes classification model. Use 0.5 as the cutoff value for the analysis. Report the accuracy, specificity, sensitivity, and precision rates (in proportions) for the validation data set.

Note: Enter your answers as decimals and round them to 2 decimal places.

b-2. Which of the following statements is most accurate?

multiple choice 1

• Using 0.5 as the cutoff value, the misclassification rate of the nave Bayes model is about 31%.
• The nave Bayes model is 64% more accurate than the nave rule (classifying all cases into the predominant class).
• Using 0.5 as the cutoff value, the nave Bayes model is able to correctly classify 81% of the customers who rent a beachfront house.
• The first 50% of the customers selected by the nave Bayes model have all rented a beachfront house.

c-1. Generate the cumulative lift chart. Is the following statement a true statement?

The cumulative lift chart shows that the nave Bayes model can identify about 105 customers who rent beachfront houses by selecting 200 of the potential customers with the highest predicted probabilities of renting a beachfront house.

multiple choice 2

• True
• False

c-2. Generate the decile-wise chart. What is the lift of the leftmost bar of the decile-wise chart?

Note: Round your answer to 2 decimal places.

d. Generate the ROC curve. What is the AUC value of the ROC curve?

Note: Round your answer to 4 decimal places.

e. Which of the following statements is least accurate?

multiple choice 3

• The ROC curve suggests that the nave Bayes model performs better than the baseline model in terms of sensitivity and specificity across all possible cutoff values.
• The cutoff value 0.5 is effective in identifying customers who would rent a beachfront house.
• The first 10% of the customers selected by the nave Bayes model include more individuals who would rent a beachfront house as compared to a randomly selected 10% of potential customers.
• The nave Bayes model is somewhat effective in predicting the likelihood of potential customer renting a beachfront home according to the following criterion: Effective if AUC 0.8; Somewhat effective if 0.5 < AUC < 0.8; Ineffective if AUC 0.5.

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.

Nora Jackson owns a number of vacation homes on a beach. She works with a consortium of rental home owners to gather a data set to build a classification model to predict the likelihood of potential customers renting a beachfront home during holidays. The accompanying data file includes the following variables: whether the potential customer owns a home (Own = 1 if yes, 0 otherwise), whether the customer has children (Children = 1 if yes, 0 otherwise), the customer's age in years (Age), annual income (Income), and whether or not the customer has previously rented a beachfront house (Rental = 1 if yes, 0 otherwise).

Click here for the Excel Data File

a. Bin the Age and Income variables. Choose the Equal count option and two bins for each of the two variables. What are the bin numbers for the Age and Income variables of the first two observations?

b-1. Partition the transformed data to develop a nave Bayes classification model. Use 0.5 as the cutoff value for the analysis. Report the accuracy, specificity, sensitivity, and precision rates (in proportions) for the validation data set.

Note: Enter your answers as decimals and round them to 2 decimal places.

b-2. Which of the following statements is most accurate?

multiple choice 1

• Using 0.5 as the cutoff value, the misclassification rate of the nave Bayes model is about 31%.
• The nave Bayes model is 64% more accurate than the nave rule (classifying all cases into the predominant class).
• Using 0.5 as the cutoff value, the nave Bayes model is able to correctly classify 81% of the customers who rent a beachfront house.
• The first 50% of the customers selected by the nave Bayes model have all rented a beachfront house.

c-1. Generate the cumulative lift chart. Is the following statement a true statement?

The cumulative lift chart shows that the nave Bayes model can identify about 105 customers who rent beachfront houses by selecting 200 of the potential customers with the highest predicted probabilities of renting a beachfront house.

multiple choice 2

• True
• False

c-2. Generate the decile-wise chart. What is the lift of the leftmost bar of the decile-wise chart?

Note: Round your answer to 2 decimal places.

d. Generate the ROC curve. What is the AUC value of the ROC curve?

Note: Round your answer to 4 decimal places.

e. Which of the following statements is least accurate?

multiple choice 3

• The ROC curve suggests that the nave Bayes model performs better than the baseline model in terms of sensitivity and specificity across all possible cutoff values.
• The cutoff value 0.5 is effective in identifying customers who would rent a beachfront house.
• The first 10% of the customers selected by the nave Bayes model include more individuals who would rent a beachfront house as compared to a randomly selected 10% of potential customers.
• The nave Bayes model is somewhat effective in predicting the likelihood of potential customer renting a beachfront home according to the following criterion: Effective if AUC 0.8; Somewhat effective if 0.5 < AUC < 0.8; Ineffective if AUC 0.5.

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.

Nora Jackson owns a number of vacation homes on a beach. She works with a consortium of rental home owners to gather a data set to build a classification model to predict the likelihood of potential customers renting a beachfront home during holidays. The accompanying data file includes the following variables: whether the potential customer owns a home (Own = 1 if yes, 0 otherwise), whether the customer has children (Children = 1 if yes, 0 otherwise), the customer's age in years (Age), annual income (Income), and whether or not the customer has previously rented a beachfront house (Rental = 1 if yes, 0 otherwise).

Click here for the Excel Data File

a. Bin the Age and Income variables. Choose the Equal count option and two bins for each of the two variables. What are the bin numbers for the Age and Income variables of the first two observations?

b-1. Partition the transformed data to develop a nave Bayes classification model. Use 0.5 as the cutoff value for the analysis. Report the accuracy, specificity, sensitivity, and precision rates (in proportions) for the validation data set.

Note: Enter your answers as decimals and round them to 2 decimal places.

b-2. Which of the following statements is most accurate?

multiple choice 1

• Using 0.5 as the cutoff value, the misclassification rate of the nave Bayes model is about 31%.
• The nave Bayes model is 64% more accurate than the nave rule (classifying all cases into the predominant class).
• Using 0.5 as the cutoff value, the nave Bayes model is able to correctly classify 81% of the customers who rent a beachfront house.
• The first 50% of the customers selected by the nave Bayes model have all rented a beachfront house.

c-1. Generate the cumulative lift chart. Is the following statement a true statement?

The cumulative lift chart shows that the nave Bayes model can identify about 105 customers who rent beachfront houses by selecting 200 of the potential customers with the highest predicted probabilities of renting a beachfront house.

multiple choice 2

• True
• False

c-2. Generate the decile-wise chart. What is the lift of the leftmost bar of the decile-wise chart?

Note: Round your answer to 2 decimal places.

d. Generate the ROC curve. What is the AUC value of the ROC curve?

Note: Round your answer to 4 decimal places.

e. Which of the following statements is least accurate?

multiple choice 3

• The ROC curve suggests that the nave Bayes model performs better than the baseline model in terms of sensitivity and specificity across all possible cutoff values.
• The cutoff value 0.5 is effective in identifying customers who would rent a beachfront house.
• The first 10% of the customers selected by the nave Bayes model include more individuals who would rent a beachfront house as compared to a randomly selected 10% of potential customers.
• The nave Bayes model is somewhat effective in predicting the likelihood of potential customer renting a beachfront home according to the following criterion: Effective if AUC 0.8; Somewhat effective if 0.5 < AUC < 0.8; Ineffective if AUC 0.5.