The answers to this problem are based on R version 3.5.3. To replicate the results with newer versions of R, execute the following line of code at the beginning of the R session or your R code: suppressWarnings(RNGversion("3.5.3")). For R, partition data sets into 60% training and 40% validation. Use the statement set.seed(1) to specify the random seed of 1 for both data partitioning and cross-validation. 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 two predictor variables (x1, x2) and the target variable (y). Partition the data to develop a nave Bayes classification model where 'Y' denotes the positive or success class for y.

a.Report the accuracy, specificity, sensitivity, and precision rates for the validation data set.(Round your final answer to 2decimal places.)

b-1.What is the area under the ROC curve (or the AUC value)?(Round your final answer to 4decimal places.)

b-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.

• True
• False

c.Report the scoring results for the five new observations.

Scoring result for first new observationScoring result for second new observationScoring result for third new observationScoring result for fourth new observationScoring result for fifth new observation