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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: How to approach this? Classification vs Recommendation
Date Sat, 19 May 2012 00:08:24 GMT
Not so trivially, these classifiers can help each other.  What you have is
a form of transduction or example based learnng.

On Fri, May 18, 2012 at 5:24 PM, Sean Owen <srowen@gmail.com> wrote:

> 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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