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 <[email protected]> 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.
