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From Sean Owen <sro...@gmail.com>
Subject Re: How to approach this? Classification vs Recommendation
Date Fri, 18 May 2012 21:24:21 GMT
Trivially it's four classifiers. You have just one input here, and
it's binary. That seems like too little info to discriminate on. All
you can learn -- and it doesn't really need a classifier algorithm --
is there's an x% chance of encountering problem a if funded, and
(100-x)% of a if not.

On Fri, May 18, 2012 at 10:00 PM, fht <lily.odriscoll@gmail.com> wrote:
> Hi,
>
> I suppose this a combination of a generic machine learning question and a
> mahout question.
>
> I have a data set. A user may or may not be part of a funded scheme.
>
> If there are not part of the funded scheme they might be susceptible to
> certain problems a, b, c and d.
> If there are part of the funded scheme they might incur problems a, b and c
> but not d.
>
> I want to process the data set to infer something like people who *are* part
> of funded scheme won't encounter problem c and d.
>
> Is this a recommendation or a classification - How do I approach this?
>
> Also can hive inteactt with mahout - I read (correct me if I'm wrong) that
> it's probably best to input data to mahout in csv format - I assume this is
> possible with hive?
>
> many thanks.
>
> --
> View this message in context: http://lucene.472066.n3.nabble.com/How-to-approach-this-Classification-vs-Recommendation-tp3984795.html
> Sent from the Mahout User List mailing list archive at Nabble.com.

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