Day Day1 Day2 Day3 Day4 Day5 Day6 Day7 Day8 Day9 Day10 Day11 Outlook Sunny Sunny Overcast Rain Rain Rain Overcast Sunny Sunny Rain Sunny Temperature Hot Hot Hot Mild Cool Cool Cool Mild Cool Mild Mild Mild Hot Mild Humidity High High High High Normal Normal Normal High Normal Normal Normal High Normal High Wind Weak Strong Weak Weak Weak Strong Strong Weak Weak Weak Strong Strong Weak Strong Play Tennis No No Yes Yes Yes No Yes No Yes Yes Yes Yes Yes No
Day12 Overcast Day13 Overcast Day14 Rain 给定与判定树归纳相同的训练数据，我们希望使用朴素贝叶斯分类预测一个未知样本的类标号。数据样本用属性Outlook，Temperature，Humidity和Wind描述。类标号属性Play_Tennis具有两个不同值（即（Yes,No））。设C1对应于类Play_Tennis=“Yes”，而C2对应于类Play_Tennis=“No”。我们希望分类的样本为
X??Outlook?\Sunny\,Temperature?Cool\,Humidity?\High\,Wind?\Strong\?
我们需要最大化P?XCi?P?Ci?，i=1,2。每个类的先验概率P(C)可以根据训练样本计算：
i
P(Play_Tennis=”Yes”)=9/14=0.643 P(Play_Tennis=”No”)=5/14=0.357
为计算P?XCi?，i=1,2，我们计算下面的条件概率： P(Outlook=”sunny”|Play_Tennis=”Yes”)=2/9=0.222 P(Outlook=”sunny”|Play_Tennis=”No”)=3/5=0.600 P(Temperature=”Cool”|Play_Tennis=”Yes”)=3/9=0.333
P(Temperature=”Cool”|Play_Tennis=”No”)=1/5=0.200 P(Hudimity=”High”|Play_Tennis=”Yes”)=3/9=0.333 P(Hudimity=”High”|Play_Tennis=”No”)=4/5=0.800 P(Wind=”Strong”|Play_Tennis=”Yes”)=3/9=0.333 P(Wind=”Strong”|Play_Tennis=”No”)=3/5=0.600 使用以上概率，我们得到：
P(X|Play_Tennis=”Yes”)=0.222×0.333×0.333×0.333=0.00823 P(X|Play_Tennis=”No”)=0.600×0.200×0.800×0.600=0.0576
P(X|Play_Tennis=”Yes”)P(Play_Tennis=”Yes”)=0.00823×0.643=0.0053 P(X|Play_Tennis=”No”)P(Play_Tennis=”No”)=0.0576×0.357=0.0206 因此，对于样本X，朴素贝叶斯分类预测Play_Tennis=”No”