# data[].attributes.body
1. P(YES|X') = (4/9 * 3/9*3/9*3/9)*9/14 = 0.01058
P(NO|X') = (0/5 * 1/5*4/5*3/5)*5/14 = 0
YES

2. P(YES|X') = (4/9 * 3/9*6/9*6/9)*9/14 = 0.04233
P(NO|X') = (0/5 * 1/5*1/5*2/5)*5/14 = 0
YES

3. P(YES|X') = (3/9 * 4/9*6/9*6/9)*9/14 = 0.04233
P(NO|X') = (2/5 * 1/5*1/5*2/5)*5/14 = 0.00457
YES

# data[].attributes.body
1.Given the fact P(Yes|x’) > P(No|x’), we label x’ to be “yES”
2.Given the fact P(Yes|x’) > P(No|x’), we label x’ to be “yES”
3.Given the fact P(Yes|x’) > P(No|x’), we label x’ to be “yES”

# data[].attributes.body
1.  Given a new instance, predict its label x’=(Outlook=Overcast, Temperature=Cool, Humidity=High, Wind=Strong)
2. Given a new instance, predict its label x’=(Outlook=Overcast, Temperature=Cool, Humidity=Normal, Wind=Weak)
3. Given a new instance, predict its label x’=(Outlook=Rain, Temperature=Mild, Humidity=Normal, Wind=Weak)