algorithm - Naive Bayes Classification: Understanding example correctly? -

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i looking multinomial model naive bayes classification, , have come across following example:

enter image description here enter image description here

i think understand everything, have developed following reasoning confirmed:

for given class c, , document d consisting of terms t1, t2, ..., tn. here how calculate p(c|d):

  • p(class | doc): (prior[c]) * (prob[t1 in c]) * (prob[t2 in c]) * ... * (prob[tn in c])

  • p (! class | doc): (prior[!c]) * (prob[t1 in !c]) * (prob[t2 in !c]) * ... * (prob[tn in !c])

is correct? , thus, reason power 3 present in both (3/7) , (2/9), denoting p(chinese|c) , p(chinese|!c) fact 'chinese' appears 3 times in d5?

thank in advance.


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