I am quite unfamiliar with using SSAS as of the moment. I am aware of the classical implementation of Naive Bayes. I have learned about it from the
here. However what I am looking for is a complete walkthrough of how to use this particular algorithm with SSAS.

For simplicity let me assume we are supposed to classify a news write-up as having positive criticism or negative criticism. So for the positive articles we can observe words like
*good, awesome, super, recommended, love, like, *etc occuring frequently. For negative articles we can observe words like
*bad, poor, unsatisfactory, unsatisfied, pathetic, *etc mostly. There are only two possible outcomes (**positive
**or **negative**), hence, generalizing on patterns is fairly simple.

To start with we have a few write-ups with their corresponding outcomes, which are
*mostly *in accordance with patterns we've generalized above. If we were to do this without the help of a data mining tool, we would do the following:

- Take the first write-up (assume this one is a positive article)
- We'd first split the whole write-ups into words.
- Remove the
*stopwords* in them, like *the, this, that, *etc. (Words meant to provide a grammatical structure to the write-up but they occur frequently hence get rid of them). We get a corpus of words now. - This corpus is assigned to the outcome
**positive**. We simply note the frequency of how many time positive appears, and also the frequency of the individual words tending to give outcome
**positive**. - The next write-up is taken. (Assume this one to be
**negative**). - Steps 2-5 is repeated and the particular frequencies are updated each time.

So once we have looked into all documents, we can actually prepare test cases.

In accordance with the formula above, **nc** is the no.of times *
good *actually give the outcome *positive*. **p **is a prior estimate (=0.5 since only 2 outcomes), and
**n **is no.of time *positive *outcome appears in our corpus.

How can I use SSAS to and go about verifying these kind of test cases manually?

I am a bundle of mistakes intertwined together with good intentions