Let me know, if you need some help. ]]>

Yet to work on the same

]]>Wow.. cool..

]]>Hahaha…

]]>Ha.. Nice to meet you too ðŸ™‚

]]>I am having two different understandings.

(A)

Is the Denominator intuitively the sum of different class probabilities.

i.e, Should we say the feature can belong to either Setosa or Virginica or Versicolor.

Now the individual class probabilities can be

(I) likelihood of the feature belonging to Setosa given it is Setosa * prior(Setosa)

(II) likelihood of the feature belonging to Virginica given it is Virginica * prior(Virginica)

(III) likelihood of the feature belonging to Versicolor given it is Versicolor * prior(Versicolor)

Now should we add these three terms to get the denominator in all three cases.

(B)

However my doubt is Should it be like for finding the probability of feature belong to setosa

the denominator should be sum of the following three probabilities.

(1) likelihood of the feature belonging to Setosa given it is Setosa * prior(Setosa)

(2) likelihood of the feature belonging to Setosa given it is Virginica * prior(Virginica)

(3) likelihood of the feature belonging to Setosa given it is Versicolor * prior(Versicolor)

But then how to calculate (2) and (3).

Thanks a lot for your time.

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