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From tuku <>
Subject Re: Mahout for text classification
Date Wed, 11 Dec 2013 10:21:08 GMT
I am currently using naive bayes for text classification.
I prefer NB over SVM because;
- SVM has long training time
- NB can be incremental
- NB can be fully parallel

the main decisions you should make while using NB is using tf or tfidf and
using binary NB or multinomial
if you classify short texts like tweets you should use tf, otherwise tfidf
there is no binary implementation for NB in mahout but I implemented one in
a very short time ( I mean it is easy ) and binary usually is better than
multinomial on text classification

If you don't know where you're going, any road will get you there
"Not all those who wander are lost." - J.R.R. Tolkien
"Fish don't know they're in water."
"Smile, breathe and go slowly." - Thich Nhat Hanh, Zen Buddhist monk
"Zamanlarını para kazanmak ve saklamakla geçirenler, sonunda, en çok
istediklerinin satın alınamayacak şeyler olduğunu anlarlar."
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in your years."
"in 20 years, you will be more dissapointed by what you didn't do than what
you did."
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On 11 December 2013 08:56, Tharindu Rusira <> wrote:

> Hello,
> I'm currently comparing Mahout classification algorithms that can be used
> in text classification? I checked [1] but many of them have open issues so
> I'm not sure which of them are in working condition and properly supported
> by Mahout.
> According to what I've found so far, SVM is preferred for text
> classification because of its ability to work with high dimensional feature
> spaces.
> Also I've gone through MAHOUT-334 and this[2] recent mail thread.
> According to the wiki, Naive Bayes seems to be a reliable candidate for a
> classification task. Could someone please provide more details on this and
> the suitability of Naive Bayes for text classification?
> Thanks,
> [1]
> [2]
> --
> M.P. Tharindu Rusira Kumara
> Department of Computer Science and Engineering,
> University of Moratuwa,
> Sri Lanka.
> +94757033733

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