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layout: default
title: ClassifyingYourData
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# Classifying data from the command line
After you've done the [Quickstart](../basics/quickstart.html) and are familiar with the basics of Mahout, it is time to build a
classifier from your own data. The following pieces *may* be useful for in getting started:
# Input
For starters, you will need your data in an appropriate Vector format: See [Creating Vectors](../basics/creating-vectors.html) as well as [Creating Vectors from Text](../basics/creating-vectors-from-text.html).
# Running the Process
* Logistic regression [background](logistic-regression.html)
* [Naive Bayes background](naivebayes.html) and [commandline](bayesian-commandline.html) options.
* [Complementary naive bayes background](complementary-naive-bayes.html), [design](https://issues.apache.org/jira/browse/mahout-60.html), and [c-bayes-commandline](c-bayes-commandline.html)
* [Random Forests Classification](https://cwiki.apache.org/confluence/display/MAHOUT/Random+Forests) comes with a [Breiman example](breiman-example.html). There is some really great documentation
over at [Mark Needham's blog](http://www.markhneedham.com/blog/2012/10/27/kaggle-digit-recognizer-mahout-random-forest-attempt/). Also checkout the description on [Xiaomeng Shawn Wan
s](http://shawnwan.wordpress.com/2012/06/01/mahout-0-7-random-forest-examples/) blog.