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From build...@apache.org
Subject svn commit: r919417 - in /websites/staging/mahout/trunk/content: ./ users/classification/bankmarketing-example.html
Date Fri, 15 Aug 2014 10:17:38 GMT
Author: buildbot
Date: Fri Aug 15 10:17:37 2014
New Revision: 919417

Log:
Staging update by buildbot for mahout

Added:
    websites/staging/mahout/trunk/content/users/classification/bankmarketing-example.html
Modified:
    websites/staging/mahout/trunk/content/   (props changed)

Propchange: websites/staging/mahout/trunk/content/
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Added: websites/staging/mahout/trunk/content/users/classification/bankmarketing-example.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/classification/bankmarketing-example.html
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+++ websites/staging/mahout/trunk/content/users/classification/bankmarketing-example.html
Fri Aug 15 10:17:37 2014
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+  <div id="content-wrap" class="clearfix">
+   <div id="main">
+    <h1 id="bank-marketing-example">Bank Marketing Example</h1>
+<h3 id="introduction">Introduction</h3>
+<p>This page describes how to run Mahout's SGD classifier on the <a href="http://mlr.cs.umass.edu/ml/datasets/Bank+Marketing">UCI
Bank Marketing dataset</a>.
+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 &amp; 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 eaxmple consists of 3 classes:</p>
+<ul>
+<li>BankMarketingClassificationMain</li>
+<li>TelephoneCall</li>
+<li>TelephoneCallParser</li>
+</ul>
+<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 ccuracy is printed to standard output.</p>
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