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Posted to user@mahout.apache.org by mandeep singh <ma...@oracle.com> on 2014/02/04 08:03:54 UTC

Naive Bayes Classifier for Marketing data

Hi,

Mahout In Action book says that naive Bayes strongly prefers text-like 
data.
Is it possible or worth to run naive bayes classifier on the marketing 
data in the following format, also mentioned in the book:
Data has these fields: age gender typeId colorId price discount 
offerTime purchaseDelay purchased
Similar data formats on which I want to run naive bayes: 
http://archive.ics.uci.edu/ml/datasets/Bank+Marketing

Thanks.

Re: Naive Bayes Classifier for Marketing data

Posted by Ted Dunning <te...@gmail.com>.
Binning refers to the reduction of a continuous variable to a categorical
variable.  This is done by defining intervals which partition all possible
values and testing the continuous variable to see which interval it falls
into.

Why would map-reduce have anything to do with this?



On Tue, Feb 4, 2014 at 5:51 AM, unmesha sreeveni <un...@gmail.com>wrote:

> Sorry.
> But in defenitive guide i saw binning tags. I dnt know about whether
> numerical binning is possible in mapreduce.
>
>
> On Tue, Feb 4, 2014 at 7:09 PM, Ted Dunning <te...@gmail.com> wrote:
>
> > Not to be rude, but how would you think you could use binning in a
> > map-reduce program?
> >
> > Seriously, what ideas can you come up with for this?  What are the
> problems
> > you see?
> >
> >
> > On Tue, Feb 4, 2014 at 4:29 AM, unmesha sreeveni <unmeshabiju@gmail.com
> > >wrote:
> >
> > > How to use binning in mapreduce
> > >
> > >
> > > On Tue, Feb 4, 2014 at 2:59 PM, Ted Dunning <te...@gmail.com>
> > wrote:
> > >
> > > > Mandeep,
> > > >
> > > > I just worked through a similar example using the same data set but
> > using
> > > > the logistic regression learner.
> > > >
> > > > In order to use Naive bayes, you would need to convert the continuous
> > > > variables to categorical variables by binning.
> > > >
> > > >
> > > >
> > > > On Mon, Feb 3, 2014 at 11:03 PM, mandeep singh
> > > > <ma...@oracle.com>wrote:
> > > >
> > > > > Hi,
> > > > >
> > > > > Mahout In Action book says that naive Bayes strongly prefers
> > text-like
> > > > > data.
> > > > > Is it possible or worth to run naive bayes classifier on the
> > marketing
> > > > > data in the following format, also mentioned in the book:
> > > > > Data has these fields: age gender typeId colorId price discount
> > > offerTime
> > > > > purchaseDelay purchased
> > > > > Similar data formats on which I want to run naive bayes:
> > > > > http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
> > > > >
> > > > > Thanks.
> > > > >
> > > >
> > >
> > >
> > >
> > > --
> > > *Thanks & Regards*
> > >
> > > Unmesha Sreeveni U.B
> > > Junior Developer
> > >
> > > http://www.unmeshasreeveni.blogspot.in/
> > >
> >
>
>
>
> --
> *Thanks & Regards*
>
> Unmesha Sreeveni U.B
> Junior Developer
>
> http://www.unmeshasreeveni.blogspot.in/
>

Re: Naive Bayes Classifier for Marketing data

Posted by unmesha sreeveni <un...@gmail.com>.
Sorry.
But in defenitive guide i saw binning tags. I dnt know about whether
numerical binning is possible in mapreduce.


On Tue, Feb 4, 2014 at 7:09 PM, Ted Dunning <te...@gmail.com> wrote:

> Not to be rude, but how would you think you could use binning in a
> map-reduce program?
>
> Seriously, what ideas can you come up with for this?  What are the problems
> you see?
>
>
> On Tue, Feb 4, 2014 at 4:29 AM, unmesha sreeveni <unmeshabiju@gmail.com
> >wrote:
>
> > How to use binning in mapreduce
> >
> >
> > On Tue, Feb 4, 2014 at 2:59 PM, Ted Dunning <te...@gmail.com>
> wrote:
> >
> > > Mandeep,
> > >
> > > I just worked through a similar example using the same data set but
> using
> > > the logistic regression learner.
> > >
> > > In order to use Naive bayes, you would need to convert the continuous
> > > variables to categorical variables by binning.
> > >
> > >
> > >
> > > On Mon, Feb 3, 2014 at 11:03 PM, mandeep singh
> > > <ma...@oracle.com>wrote:
> > >
> > > > Hi,
> > > >
> > > > Mahout In Action book says that naive Bayes strongly prefers
> text-like
> > > > data.
> > > > Is it possible or worth to run naive bayes classifier on the
> marketing
> > > > data in the following format, also mentioned in the book:
> > > > Data has these fields: age gender typeId colorId price discount
> > offerTime
> > > > purchaseDelay purchased
> > > > Similar data formats on which I want to run naive bayes:
> > > > http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
> > > >
> > > > Thanks.
> > > >
> > >
> >
> >
> >
> > --
> > *Thanks & Regards*
> >
> > Unmesha Sreeveni U.B
> > Junior Developer
> >
> > http://www.unmeshasreeveni.blogspot.in/
> >
>



-- 
*Thanks & Regards*

Unmesha Sreeveni U.B
Junior Developer

http://www.unmeshasreeveni.blogspot.in/

Re: Naive Bayes Classifier for Marketing data

Posted by Ted Dunning <te...@gmail.com>.
Not to be rude, but how would you think you could use binning in a
map-reduce program?

Seriously, what ideas can you come up with for this?  What are the problems
you see?


On Tue, Feb 4, 2014 at 4:29 AM, unmesha sreeveni <un...@gmail.com>wrote:

> How to use binning in mapreduce
>
>
> On Tue, Feb 4, 2014 at 2:59 PM, Ted Dunning <te...@gmail.com> wrote:
>
> > Mandeep,
> >
> > I just worked through a similar example using the same data set but using
> > the logistic regression learner.
> >
> > In order to use Naive bayes, you would need to convert the continuous
> > variables to categorical variables by binning.
> >
> >
> >
> > On Mon, Feb 3, 2014 at 11:03 PM, mandeep singh
> > <ma...@oracle.com>wrote:
> >
> > > Hi,
> > >
> > > Mahout In Action book says that naive Bayes strongly prefers text-like
> > > data.
> > > Is it possible or worth to run naive bayes classifier on the marketing
> > > data in the following format, also mentioned in the book:
> > > Data has these fields: age gender typeId colorId price discount
> offerTime
> > > purchaseDelay purchased
> > > Similar data formats on which I want to run naive bayes:
> > > http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
> > >
> > > Thanks.
> > >
> >
>
>
>
> --
> *Thanks & Regards*
>
> Unmesha Sreeveni U.B
> Junior Developer
>
> http://www.unmeshasreeveni.blogspot.in/
>

Re: Naive Bayes Classifier for Marketing data

Posted by unmesha sreeveni <un...@gmail.com>.
How to use binning in mapreduce


On Tue, Feb 4, 2014 at 2:59 PM, Ted Dunning <te...@gmail.com> wrote:

> Mandeep,
>
> I just worked through a similar example using the same data set but using
> the logistic regression learner.
>
> In order to use Naive bayes, you would need to convert the continuous
> variables to categorical variables by binning.
>
>
>
> On Mon, Feb 3, 2014 at 11:03 PM, mandeep singh
> <ma...@oracle.com>wrote:
>
> > Hi,
> >
> > Mahout In Action book says that naive Bayes strongly prefers text-like
> > data.
> > Is it possible or worth to run naive bayes classifier on the marketing
> > data in the following format, also mentioned in the book:
> > Data has these fields: age gender typeId colorId price discount offerTime
> > purchaseDelay purchased
> > Similar data formats on which I want to run naive bayes:
> > http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
> >
> > Thanks.
> >
>



-- 
*Thanks & Regards*

Unmesha Sreeveni U.B
Junior Developer

http://www.unmeshasreeveni.blogspot.in/

Re: Naive Bayes Classifier for Marketing data

Posted by Ted Dunning <te...@gmail.com>.
Mandeep,

I just worked through a similar example using the same data set but using
the logistic regression learner.

In order to use Naive bayes, you would need to convert the continuous
variables to categorical variables by binning.



On Mon, Feb 3, 2014 at 11:03 PM, mandeep singh
<ma...@oracle.com>wrote:

> Hi,
>
> Mahout In Action book says that naive Bayes strongly prefers text-like
> data.
> Is it possible or worth to run naive bayes classifier on the marketing
> data in the following format, also mentioned in the book:
> Data has these fields: age gender typeId colorId price discount offerTime
> purchaseDelay purchased
> Similar data formats on which I want to run naive bayes:
> http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
>
> Thanks.
>