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 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.

x1	x2
A	0
B	1
C	1
B	0
C	0

y	x1	x2
Y	A	1
Y	B	0
Y	C	0
Y	B	0
N	C	0
N	A	0
N	A	1
N	A	1
N	B	1
Y	C	0
N	B	1
Y	C	0
N	B	1
Y	C	0
N	A	1
Y	C	1
N	A	1
Y	C	0
N	C	0
Y	C	1
N	B	1
N	B	0
N	B	1
Y	A	0
Y	C	1
Y	C	0
N	C	0
N	B	1
N	B	1
N	A	0
Y	C	0
N	B	0
N	C	1
Y	B	1
Y	A	0
Y	A	1
N	B	0
N	C	1
Y	C	0
N	B	1
Y	B	1
N	A	1
Y	A	1
N	A	0
Y	A	0
N	B	0
N	B	1
N	A	1
Y	C	0
Y	A	0
N	B	0
N	A	0
Y	B	1
Y	B	1
Y	C	1
Y	C	0
Y	B	0
N	A	1
Y	A	1
N	B	0
N	C	0
Y	A	0
N	A	1
N	C	0
N	C	1
Y	C	1
Y	C	0
Y	B	0
N	B	0
Y	C	0
N	C	0
Y	A	0
N	C	1
N	B	0
N	C	0

a. Report the accuracy, specificity, sensitivity, and precision for the validation data set. (Enter your answers as decimals and round them to 2 decimal places.) b-1. What is the area under the ROC curve (or AUC value)? (Round your answer to 4 decimal places.)

b-2. Is the following statement a true statement? The ROC curve shows that in general the nave Bayes model performs better than the baseline model in terms sensitivity and specificity. TRUE OR 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