We will train a Naive Bayes classifier to predict class labelsYas a function of input featuresF_i.

We are given the following 15 training points:

Use Laplace smoothing with strengthk= 2 to estimate the prior()for the same data.

(=):

(=):

(=):

Use Laplace Smoothing with strengthk= 2 to estimate the conditional probability distributions (again, the second two are provided).

(1=0|=):

(1=1|=):

(1=0|=):

(1=1|=):

(1=0|=):

(1=1|=):

F2	Y	(2|)
0	A	.5333
1	A	.4667
0	B	.6000
1	B	.4000
0	C	.7143
1	C	.2857

F3	Y	(3|)
0	A	.4667
1	A	.5333
0	B	.4000
1	B	.6000
0	C	.5714
1	C	.4286

Now consider a new data point(1=1,2=1,3=1). Use your classifier to determine the joint probability of causesYand this new data point, along with the posterior probability ofYgiven the new data:

(=,1=1,2=1,3=1):

(=,1=1,2=1,3=1):

(=,1=1,2=1,3=1):

(=|1=1,2=1,3=1):

(=|1=1,2=1,3=1):

(=|1=1,2=1,3=1):

What label does your classifier give to the new data point (break ties alphabetically)?