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Posted to commits@mahout.apache.org by bu...@apache.org on 2015/03/19 22:21:47 UTC
svn commit: r944380 [8/24] - in /websites/staging/mahout/trunk/content: ./
developers/ general/ users/basics/ users/classification/ users/clustering/
users/dim-reduction/ users/mapreduce/ users/mapreduce/classification/
users/mapreduce/clustering/ user...
Added: websites/staging/mahout/trunk/content/users/mapreduce/classification/bankmarketing-example.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/mapreduce/classification/bankmarketing-example.html (added)
+++ websites/staging/mahout/trunk/content/users/mapreduce/classification/bankmarketing-example.html Thu Mar 19 21:21:45 2015
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+The goal is to predict if the client will subscribe a term deposit offered via a phone call. The features in the dataset consist
+of information such as age, job, marital status as well as information about the last contacts from the bank.</p>
+<h3 id="code-data">Code & Data</h3>
+<p>The bank marketing example code lives under </p>
+<p><em>mahout-examples/src/main/java/org.apache.mahout.classifier.sgd.bankmarketing</em></p>
+<p>The data can be found at </p>
+<p><em>mahout-examples/src/main/resources/bank-full.csv</em></p>
+<h3 id="code-details">Code details</h3>
+<p>This example consists of 3 classes:</p>
+<ul>
+<li>BankMarketingClassificationMain</li>
+<li>TelephoneCall</li>
+<li>TelephoneCallParser</li>
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+<p>When you run the main method of BankMarketingClassificationMain it parses the dataset using the TelephoneCallParser and trains
+a logistic regression model with 20 runs and 20 passes. The TelephoneCallParser uses Mahout's feature vector encoder
+to encode the features in the dataset into a vector. Afterwards the model is tested and the learning rate and AUC is printed accuracy is printed to standard output.</p>
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--- websites/staging/mahout/trunk/content/users/mapreduce/classification/bayesian-commandline.html (added)
+++ websites/staging/mahout/trunk/content/users/mapreduce/classification/bayesian-commandline.html Thu Mar 19 21:21:45 2015
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+ <li class="divider"></li>
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+ <li><a href="/users/mapreduce/recommender/recommender-documentation.html">Overview</a></li>
+ <li class="divider"></li>
+ <li class="nav-header">Hadoop</li>
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+
+ <div id="content-wrap" class="clearfix">
+ <div id="main">
+ <h1 id="naive-bayes-commandline-documentation">Naive Bayes commandline documentation</h1>
+<p><a name="bayesian-commandline-Introduction"></a></p>
+<h2 id="introduction">Introduction</h2>
+<p>This quick start page describes how to run the naive bayesian and
+complementary naive bayesian classification algorithms on a Hadoop cluster.</p>
+<p><a name="bayesian-commandline-Steps"></a></p>
+<h2 id="steps">Steps</h2>
+<p><a name="bayesian-commandline-Testingitononesinglemachinew/ocluster"></a></p>
+<h3 id="testing-it-on-one-single-machine-wo-cluster">Testing it on one single machine w/o cluster</h3>
+<p>In the examples directory type:</p>
+<div class="codehilite"><pre><span class="n">mvn</span> <span class="o">-</span><span class="n">q</span> <span class="n">exec</span><span class="p">:</span><span class="n">java</span>
+ <span class="o">-</span><span class="n">Dexec</span><span class="p">.</span><span class="n">mainClass</span><span class="p">=</span>"<span class="n">org</span><span class="p">.</span><span class="n">apache</span><span class="p">.</span><span class="n">mahout</span><span class="p">.</span><span class="n">classifier</span><span class="p">.</span><span class="n">bayes</span><span class="p">.</span><span class="n">mapreduce</span><span class="p">.</span><span class="n">bayes</span><span class="p">.</span><span class="o"><</span><span class="n">JOB</span><span class="o">></span>"
+ <span class="o">-</span><span class="n">Dexec</span><span class="p">.</span><span class="n">args</span><span class="p">=</span>"<span class="o"><</span><span class="n">OPTIONS</span><span class="o">></span>"
+
+<span class="n">mvn</span> <span class="o">-</span><span class="n">q</span> <span class="n">exec</span><span class="p">:</span><span class="n">java</span>
+ <span class="o">-</span><span class="n">Dexec</span><span class="p">.</span><span class="n">mainClass</span><span class="p">=</span>"<span class="n">org</span><span class="p">.</span><span class="n">apache</span><span class="p">.</span><span class="n">mahout</span><span class="p">.</span><span class="n">classifier</span><span class="p">.</span><span class="n">bayes</span><span class="p">.</span><span class="n">mapreduce</span><span class="p">.</span><span class="n">cbayes</span><span class="p">.</span><span class="o"><</span><span class="n">JOB</span><span class="o">></span>"
+ <span class="o">-</span><span class="n">Dexec</span><span class="p">.</span><span class="n">args</span><span class="p">=</span>"<span class="o"><</span><span class="n">OPTIONS</span><span class="o">></span>"
+</pre></div>
+
+
+<p><a name="bayesian-commandline-Runningitonthecluster"></a></p>
+<h3 id="running-it-on-the-cluster">Running it on the cluster</h3>
+<ul>
+<li>
+<p>In $MAHOUT_HOME/, build the jar containing the job (mvn install) The job
+will be generated in $MAHOUT_HOME/core/target/ and it's name will contain
+the Mahout version number. For example, when using Mahout 0.1 release, the
+job will be mahout-core-0.1.jar</p>
+</li>
+<li>
+<p>(Optional) 1 Start up Hadoop: $HADOOP_HOME/bin/start-all.sh</p>
+</li>
+<li>
+<p>Put the data: $HADOOP_HOME/bin/hadoop fs -put <PATH TO DATA> testdata</p>
+</li>
+<li>
+<p>Run the Job: $HADOOP_HOME/bin/hadoop jar</p>
+<p>$MAHOUT_HOME/core/target/mahout-core-<MAHOUT VERSION>.job
+ org.apache.mahout.classifier.bayes.mapreduce.bayes.BayesDriver <OPTIONS></p>
+</li>
+<li>
+<p>Get the data out of HDFS and have a look. Use bin/hadoop fs -lsr output
+to view all outputs.</p>
+</li>
+</ul>
+<p><a name="bayesian-commandline-Commandlineoptions"></a></p>
+<h2 id="command-line-options">Command line options</h2>
+<div class="codehilite"><pre><span class="n">BayesDriver</span><span class="p">,</span> <span class="n">BayesThetaNormalizerDriver</span><span class="p">,</span> <span class="n">CBayesNormalizedWeightDriver</span><span class="p">,</span> <span class="n">CBayesDriver</span><span class="p">,</span> <span class="n">CBayesThetaDriver</span><span class="p">,</span> <span class="n">CBayesThetaNormalizerDriver</span><span class="p">,</span> <span class="n">BayesWeightSummerDriver</span><span class="p">,</span> <span class="n">BayesFeatureDriver</span><span class="p">,</span> <span class="n">BayesTfIdfDriver</span> <span class="n">Usage</span><span class="p">:</span>
+ <span class="p">[</span><span class="o">--</span><span class="n">input</span> <span class="o"><</span><span class="n">input</span><span class="o">></span> <span class="o">--</span><span class="n">output</span> <span class="o"><</span><span class="n">output</span><span class="o">></span> <span class="o">--</span><span class="n">help</span><span class="p">]</span>
+
+<span class="n">Options</span>
+
+ <span class="o">--</span><span class="n">input</span> <span class="p">(</span><span class="o">-</span><span class="nb">i</span><span class="p">)</span> <span class="n">input</span> <span class="n">The</span> <span class="n">Path</span> <span class="k">for</span> <span class="n">input</span> <span class="n">Vectors</span><span class="p">.</span> <span class="n">Must</span> <span class="n">be</span> <span class="n">a</span> <span class="n">SequenceFile</span> <span class="n">of</span> <span class="n">Writable</span><span class="p">,</span> <span class="n">Vector</span><span class="p">.</span>
+ <span class="o">--</span><span class="n">output</span> <span class="p">(</span><span class="o">-</span><span class="n">o</span><span class="p">)</span> <span class="n">output</span> <span class="n">The</span> <span class="n">directory</span> <span class="n">pathname</span> <span class="k">for</span> <span class="n">output</span> <span class="n">points</span><span class="p">.</span>
+ <span class="o">--</span><span class="n">help</span> <span class="p">(</span><span class="o">-</span><span class="n">h</span><span class="p">)</span> <span class="n">Print</span> <span class="n">out</span> <span class="n">help</span><span class="p">.</span>
+</pre></div>
+ </div>
+ </div>
+</div>
+ <footer class="footer" align="center">
+ <div class="container">
+ <p>
+ Copyright © 2014 The Apache Software Foundation, Licensed under
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Added: websites/staging/mahout/trunk/content/users/mapreduce/classification/bayesian.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/mapreduce/classification/bayesian.html (added)
+++ websites/staging/mahout/trunk/content/users/mapreduce/classification/bayesian.html Thu Mar 19 21:21:45 2015
@@ -0,0 +1,432 @@
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+ <div id="main">
+ <h1 id="naive-bayes">Naive Bayes</h1>
+<h2 id="intro">Intro</h2>
+<p>Mahout currently has two Naive Bayes implementations. The first is standard Multinomial Naive Bayes. The second is an implementation of Transformed Weight-normalized Complement Naive Bayes as introduced by Rennie et al. <a href="http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf">[1]</a>. We refer to the former as Bayes and the latter as CBayes.</p>
+<p>Where Bayes has long been a standard in text classification, CBayes is an extension of Bayes that performs particularly well on datasets with skewed classes and has been shown to be competitive with algorithms of higher complexity such as Support Vector Machines. </p>
+<h2 id="implementations">Implementations</h2>
+<p>Both Bayes and CBayes are currently trained via MapReduce Jobs. Testing and classification can be done via a MapReduce Job or sequentially. Mahout provides CLI drivers for preprocessing, training and testing. A Spark implementation is currently in the works (<a href="https://issues.apache.org/jira/browse/MAHOUT-1493">MAHOUT-1493</a>).</p>
+<h2 id="preprocessing-and-algorithm">Preprocessing and Algorithm</h2>
+<p>As described in <a href="http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf">[1]</a> Mahout Naive Bayes is broken down into the following steps (assignments are over all possible index values): </p>
+<ul>
+<li>Let <code>\(\vec{d}=(\vec{d_1},...,\vec{d_n})\)</code> be a set of documents; <code>\(d_{ij}\)</code> is the count of word <code>\(i\)</code> in document <code>\(j\)</code>.</li>
+<li>Let <code>\(\vec{y}=(y_1,...,y_n)\)</code> be their labels.</li>
+<li>Let <code>\(\alpha_i\)</code> be a smoothing parameter for all words in the vocabulary; let <code>\(\alpha=\sum_i{\alpha_i}\)</code>. </li>
+<li><strong>Preprocessing</strong>(via seq2Sparse) TF-IDF transformation and L2 length normalization of <code>\(\vec{d}\)</code><ol>
+<li><code>\(d_{ij} = \sqrt{d_{ij}}\)</code> </li>
+<li><code>\(d_{ij} = d_{ij}\left(\log{\frac{\sum_k1}{\sum_k\delta_{ik}+1}}+1\right)\)</code> </li>
+<li><code>\(d_{ij} =\frac{d_{ij}}{\sqrt{\sum_k{d_{kj}^2}}}\)</code> </li>
+</ol>
+</li>
+<li><strong>Training: Bayes</strong><code>\((\vec{d},\vec{y})\)</code> calculate term weights <code>\(w_{ci}\)</code> as:<ol>
+<li><code>\(\hat\theta_{ci}=\frac{d_{ic}+\alpha_i}{\sum_k{d_{kc}}+\alpha}\)</code></li>
+<li><code>\(w_{ci}=\log{\hat\theta_{ci}}\)</code></li>
+</ol>
+</li>
+<li><strong>Training: CBayes</strong><code>\((\vec{d},\vec{y})\)</code> calculate term weights <code>\(w_{ci}\)</code> as:<ol>
+<li><code>\(\hat\theta_{ci} = \frac{\sum_{j:y_j\neq c}d_{ij}+\alpha_i}{\sum_{j:y_j\neq c}{\sum_k{d_{kj}}}+\alpha}\)</code></li>
+<li><code>\(w_{ci}=-\log{\hat\theta_{ci}}\)</code></li>
+<li><code>\(w_{ci}=\frac{w_{ci}}{\sum_i \lvert w_{ci}\rvert}\)</code></li>
+</ol>
+</li>
+<li><strong>Label Assignment/Testing:</strong><ol>
+<li>Let <code>\(\vec{t}= (t_1,...,t_n)\)</code> be a test document; let <code>\(t_i\)</code> be the count of the word <code>\(t\)</code>.</li>
+<li>Label the document according to <code>\(l(t)=\arg\max_c \sum\limits_{i} t_i w_{ci}\)</code></li>
+</ol>
+</li>
+</ul>
+<p>As we can see, the main difference between Bayes and CBayes is the weight calculation step. Where Bayes weighs terms more heavily based on the likelihood that they belong to class <code>\(c\)</code>, CBayes seeks to maximize term weights on the likelihood that they do not belong to any other class. </p>
+<h2 id="running-from-the-command-line">Running from the command line</h2>
+<p>Mahout provides CLI drivers for all above steps. Here we will give a simple overview of Mahout CLI commands used to preprocess the data, train the model and assign labels to the training set. An <a href="https://github.com/apache/mahout/blob/master/examples/bin/classify-20newsgroups.sh">example script</a> is given for the full process from data acquisition through classification of the classic <a href="https://mahout.apache.org/users/classification/twenty-newsgroups.html">20 Newsgroups corpus</a>. </p>
+<ul>
+<li>
+<p><strong>Preprocessing:</strong>
+For a set of Sequence File Formatted documents in PATH_TO_SEQUENCE_FILES the <a href="https://mahout.apache.org/users/basics/creating-vectors-from-text.html">mahout seq2sparse</a> command performs the TF-IDF transformations (-wt tfidf option) and L2 length normalization (-n 2 option) as follows:</p>
+<div class="codehilite"><pre>mahout seq2sparse
+ -i <span class="cp">${</span><span class="n">PATH_TO_SEQUENCE_FILES</span><span class="cp">}</span>
+ -o <span class="cp">${</span><span class="n">PATH_TO_TFIDF_VECTORS</span><span class="cp">}</span>
+ -nv
+ -n 2
+ -wt tfidf
+</pre></div>
+
+
+</li>
+<li>
+<p><strong>Training:</strong>
+The model is then trained using <code>mahout trainnb</code> . The default is to train a Bayes model. The -c option is given to train a CBayes model:</p>
+<div class="codehilite"><pre>mahout trainnb
+ -i <span class="cp">${</span><span class="n">PATH_TO_TFIDF_VECTORS</span><span class="cp">}</span>
+ -el
+ -o <span class="cp">${</span><span class="n">PATH_TO_MODEL</span><span class="cp">}</span>/model
+ -li <span class="cp">${</span><span class="n">PATH_TO_MODEL</span><span class="cp">}</span>/labelindex
+ -ow
+ -c
+</pre></div>
+
+
+</li>
+<li>
+<p><strong>Label Assignment/Testing:</strong>
+Classification and testing on a holdout set can then be performed via <code>mahout testnb</code>. Again, the -c option indicates that the model is CBayes. The -seq option tells <code>mahout testnb</code> to run sequentially:</p>
+<div class="codehilite"><pre>mahout testnb
+ -i <span class="cp">${</span><span class="n">PATH_TO_TFIDF_TEST_VECTORS</span><span class="cp">}</span>
+ -m <span class="cp">${</span><span class="n">PATH_TO_MODEL</span><span class="cp">}</span>/model
+ -l <span class="cp">${</span><span class="n">PATH_TO_MODEL</span><span class="cp">}</span>/labelindex
+ -ow
+ -o <span class="cp">${</span><span class="n">PATH_TO_OUTPUT</span><span class="cp">}</span>
+ -c
+ -seq
+</pre></div>
+
+
+</li>
+</ul>
+<h2 id="command-line-options">Command line options</h2>
+<ul>
+<li><strong>Preprocessing:</strong></li>
+</ul>
+<p>Only relevant parameters used for Bayes/CBayes as detailed above are shown. Several other transformations can be performed by <code>mahout seq2sparse</code> and used as input to Bayes/CBayes. For a full list of <code>mahout seq2Sparse</code> options see the <a href="https://mahout.apache.org/users/basics/creating-vectors-from-text.html">Creating vectors from text</a> page.</p>
+<div class="codehilite"><pre> <span class="n">mahout</span> <span class="n">seq2sparse</span>
+ <span class="o">--</span><span class="n">output</span> <span class="p">(</span><span class="o">-</span><span class="n">o</span><span class="p">)</span> <span class="n">output</span> <span class="n">The</span> <span class="n">directory</span> <span class="n">pathname</span> <span class="k">for</span> <span class="n">output</span><span class="p">.</span>
+ <span class="o">--</span><span class="n">input</span> <span class="p">(</span><span class="o">-</span><span class="nb">i</span><span class="p">)</span> <span class="n">input</span> <span class="n">Path</span> <span class="n">to</span> <span class="n">job</span> <span class="n">input</span> <span class="n">directory</span><span class="p">.</span>
+ <span class="o">--</span><span class="n">weight</span> <span class="p">(</span><span class="o">-</span><span class="n">wt</span><span class="p">)</span> <span class="n">weight</span> <span class="n">The</span> <span class="n">kind</span> <span class="n">of</span> <span class="n">weight</span> <span class="n">to</span> <span class="n">use</span><span class="p">.</span> <span class="n">Currently</span> <span class="n">TF</span>
+ <span class="n">or</span> <span class="n">TFIDF</span><span class="p">.</span> <span class="n">Default</span><span class="p">:</span> <span class="n">TFIDF</span>
+ <span class="o">--</span><span class="n">norm</span> <span class="p">(</span><span class="o">-</span><span class="n">n</span><span class="p">)</span> <span class="n">norm</span> <span class="n">The</span> <span class="n">norm</span> <span class="n">to</span> <span class="n">use</span><span class="p">,</span> <span class="n">expressed</span> <span class="n">as</span> <span class="n">either</span> <span class="n">a</span>
+ <span class="n">float</span> <span class="n">or</span> "<span class="n">INF</span>" <span class="k">if</span> <span class="n">you</span> <span class="n">want</span> <span class="n">to</span> <span class="n">use</span> <span class="n">the</span>
+ <span class="n">Infinite</span> <span class="n">norm</span><span class="p">.</span> <span class="n">Must</span> <span class="n">be</span> <span class="n">greater</span> <span class="n">or</span> <span class="n">equal</span>
+ <span class="n">to</span> 0<span class="p">.</span> <span class="n">The</span> <span class="n">default</span> <span class="n">is</span> <span class="n">not</span> <span class="n">to</span> <span class="n">normalize</span>
+ <span class="o">--</span><span class="n">overwrite</span> <span class="p">(</span><span class="o">-</span><span class="n">ow</span><span class="p">)</span> <span class="n">If</span> <span class="n">set</span><span class="p">,</span> <span class="n">overwrite</span> <span class="n">the</span> <span class="n">output</span> <span class="n">directory</span>
+ <span class="o">--</span><span class="n">sequentialAccessVector</span> <span class="p">(</span><span class="o">-</span><span class="n">seq</span><span class="p">)</span> <span class="p">(</span><span class="n">Optional</span><span class="p">)</span> <span class="n">Whether</span> <span class="n">output</span> <span class="n">vectors</span> <span class="n">should</span>
+ <span class="n">be</span> <span class="n">SequentialAccessVectors</span><span class="p">.</span> <span class="n">If</span> <span class="n">set</span> <span class="n">true</span>
+ <span class="k">else</span> <span class="n">false</span>
+ <span class="o">--</span><span class="n">namedVector</span> <span class="p">(</span><span class="o">-</span><span class="n">nv</span><span class="p">)</span> <span class="p">(</span><span class="n">Optional</span><span class="p">)</span> <span class="n">Whether</span> <span class="n">output</span> <span class="n">vectors</span> <span class="n">should</span>
+ <span class="n">be</span> <span class="n">NamedVectors</span><span class="p">.</span> <span class="n">If</span> <span class="n">set</span> <span class="n">true</span> <span class="k">else</span> <span class="n">false</span>
+</pre></div>
+
+
+<ul>
+<li>
+<p><strong>Training:</strong></p>
+<div class="codehilite"><pre><span class="n">mahout</span> <span class="n">trainnb</span>
+ <span class="o">--</span><span class="n">input</span> <span class="p">(</span><span class="o">-</span><span class="nb">i</span><span class="p">)</span> <span class="n">input</span> <span class="n">Path</span> <span class="n">to</span> <span class="n">job</span> <span class="n">input</span> <span class="n">directory</span><span class="p">.</span>
+ <span class="o">--</span><span class="n">output</span> <span class="p">(</span><span class="o">-</span><span class="n">o</span><span class="p">)</span> <span class="n">output</span> <span class="n">The</span> <span class="n">directory</span> <span class="n">pathname</span> <span class="k">for</span> <span class="n">output</span><span class="p">.</span>
+ <span class="o">--</span><span class="n">labels</span> <span class="p">(</span><span class="o">-</span><span class="n">l</span><span class="p">)</span> <span class="n">labels</span> <span class="n">Comma</span><span class="o">-</span><span class="n">separated</span> <span class="n">list</span> <span class="n">of</span> <span class="n">labels</span> <span class="n">to</span> <span class="n">include</span> <span class="n">in</span>
+ <span class="n">training</span>
+ <span class="o">--</span><span class="n">extractLabels</span> <span class="p">(</span><span class="o">-</span><span class="n">el</span><span class="p">)</span> <span class="n">Extract</span> <span class="n">the</span> <span class="n">labels</span> <span class="n">from</span> <span class="n">the</span> <span class="n">input</span>
+ <span class="o">--</span><span class="n">alphaI</span> <span class="p">(</span><span class="o">-</span><span class="n">a</span><span class="p">)</span> <span class="n">alphaI</span> <span class="n">Smoothing</span> <span class="n">parameter</span><span class="p">.</span> <span class="n">Default</span> <span class="n">is</span> 1<span class="p">.</span>0
+ <span class="o">--</span><span class="n">trainComplementary</span> <span class="p">(</span><span class="o">-</span><span class="n">c</span><span class="p">)</span> <span class="n">Train</span> <span class="n">complementary</span>? <span class="n">Default</span> <span class="n">is</span> <span class="n">false</span><span class="p">.</span>
+ <span class="o">--</span><span class="n">labelIndex</span> <span class="p">(</span><span class="o">-</span><span class="n">li</span><span class="p">)</span> <span class="n">labelIndex</span> <span class="n">The</span> <span class="n">path</span> <span class="n">to</span> <span class="n">store</span> <span class="n">the</span> <span class="n">label</span> <span class="n">index</span> <span class="n">in</span>
+ <span class="o">--</span><span class="n">overwrite</span> <span class="p">(</span><span class="o">-</span><span class="n">ow</span><span class="p">)</span> <span class="n">If</span> <span class="n">present</span><span class="p">,</span> <span class="n">overwrite</span> <span class="n">the</span> <span class="n">output</span> <span class="n">directory</span>
+ <span class="n">before</span> <span class="n">running</span> <span class="n">job</span>
+ <span class="o">--</span><span class="n">help</span> <span class="p">(</span><span class="o">-</span><span class="n">h</span><span class="p">)</span> <span class="n">Print</span> <span class="n">out</span> <span class="n">help</span>
+ <span class="o">--</span><span class="n">tempDir</span> <span class="n">tempDir</span> <span class="n">Intermediate</span> <span class="n">output</span> <span class="n">directory</span>
+ <span class="o">--</span><span class="n">startPhase</span> <span class="n">startPhase</span> <span class="n">First</span> <span class="n">phase</span> <span class="n">to</span> <span class="n">run</span>
+ <span class="o">--</span><span class="n">endPhase</span> <span class="n">endPhase</span> <span class="n">Last</span> <span class="n">phase</span> <span class="n">to</span> <span class="n">run</span>
+</pre></div>
+
+
+</li>
+<li>
+<p><strong>Testing:</strong></p>
+<div class="codehilite"><pre><span class="n">mahout</span> <span class="n">testnb</span>
+ <span class="o">--</span><span class="n">input</span> <span class="p">(</span><span class="o">-</span><span class="nb">i</span><span class="p">)</span> <span class="n">input</span> <span class="n">Path</span> <span class="n">to</span> <span class="n">job</span> <span class="n">input</span> <span class="n">directory</span><span class="p">.</span>
+ <span class="o">--</span><span class="n">output</span> <span class="p">(</span><span class="o">-</span><span class="n">o</span><span class="p">)</span> <span class="n">output</span> <span class="n">The</span> <span class="n">directory</span> <span class="n">pathname</span> <span class="k">for</span> <span class="n">output</span><span class="p">.</span>
+ <span class="o">--</span><span class="n">overwrite</span> <span class="p">(</span><span class="o">-</span><span class="n">ow</span><span class="p">)</span> <span class="n">If</span> <span class="n">present</span><span class="p">,</span> <span class="n">overwrite</span> <span class="n">the</span> <span class="n">output</span> <span class="n">directory</span>
+ <span class="n">before</span> <span class="n">running</span> <span class="n">job</span>
+
+ <span class="o">--</span><span class="n">model</span> <span class="p">(</span><span class="o">-</span><span class="n">m</span><span class="p">)</span> <span class="n">model</span> <span class="n">The</span> <span class="n">path</span> <span class="n">to</span> <span class="n">the</span> <span class="n">model</span> <span class="n">built</span> <span class="n">during</span> <span class="n">training</span>
+ <span class="o">--</span><span class="n">testComplementary</span> <span class="p">(</span><span class="o">-</span><span class="n">c</span><span class="p">)</span> <span class="n">Test</span> <span class="n">complementary</span>? <span class="n">Default</span> <span class="n">is</span> <span class="n">false</span><span class="p">.</span>
+ <span class="o">--</span><span class="n">runSequential</span> <span class="p">(</span><span class="o">-</span><span class="n">seq</span><span class="p">)</span> <span class="n">Run</span> <span class="n">sequential</span>?
+ <span class="o">--</span><span class="n">labelIndex</span> <span class="p">(</span><span class="o">-</span><span class="n">l</span><span class="p">)</span> <span class="n">labelIndex</span> <span class="n">The</span> <span class="n">path</span> <span class="n">to</span> <span class="n">the</span> <span class="n">location</span> <span class="n">of</span> <span class="n">the</span> <span class="n">label</span> <span class="n">index</span>
+ <span class="o">--</span><span class="n">help</span> <span class="p">(</span><span class="o">-</span><span class="n">h</span><span class="p">)</span> <span class="n">Print</span> <span class="n">out</span> <span class="n">help</span>
+ <span class="o">--</span><span class="n">tempDir</span> <span class="n">tempDir</span> <span class="n">Intermediate</span> <span class="n">output</span> <span class="n">directory</span>
+ <span class="o">--</span><span class="n">startPhase</span> <span class="n">startPhase</span> <span class="n">First</span> <span class="n">phase</span> <span class="n">to</span> <span class="n">run</span>
+ <span class="o">--</span><span class="n">endPhase</span> <span class="n">endPhase</span> <span class="n">Last</span> <span class="n">phase</span> <span class="n">to</span> <span class="n">run</span>
+</pre></div>
+
+
+</li>
+</ul>
+<h2 id="examples">Examples</h2>
+<p>Mahout provides an example for Naive Bayes classification:</p>
+<ol>
+<li><a href="twenty-newsgroups.html">Classify 20 Newsgroups</a></li>
+</ol>
+<h2 id="references">References</h2>
+<p>[1]: Jason D. M. Rennie, Lawerence Shih, Jamie Teevan, David Karger (2003). <a href="http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf">Tackling the Poor Assumptions of Naive Bayes Text Classifiers</a>. Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003).</p>
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