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.

The accompanying data set contains three predictor variables (x₁, x₂, and x₃) and the target variable (y).

Click here for the Excel Data File

a. Bin predictor variables x₁, x₂, and x₃. Choose the Equal interval option and 2 bins for each of the three variables. What are the bin numbers for the variables of the first two observations?

b. Partition the data to develop a nave Bayes classification model where "1" denotes the positive or success class for y. 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.

c-1. Generate the ROC curve. What is the area under the ROC curve (or the AUC value)?

Note: Round your answer to 4 decimal places.

c-2. Is the following statement a true statement?

The ROC curve shows that the nave Bayes model performs better than the baseline model in terms sensitivity and specificity across all possible cutoff values.

multiple choice

• True
• False

d. Change the cutoff value to 0.4. 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.