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href="#">Demo Tutorials</a><span class="spacer">&gt;</span></li><li><span 
class="last">Text Classification Engine Tutorial</span></li></ul></div><div 
id="page-title"><h1>Text Classification Engine Tu
 torial</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#introduction">Introduction</a> </li> <li> <a 
href="#prerequisites">Prerequisites</a> </li> <li> <a 
href="#engine-overview">Engine Overview</a> </li> <li> <a 
href="#quick-start">Quick Start</a> </li> </ul> </li> <li> <a 
href="#detailed-explanation-of-dase">Detailed Explanation of DASE</a> <ul> <li> 
<a href="#importing-data">Importing Data</a> </li> <li> <a 
href="#data-source-reading-event-data">Data Source: Reading Event Data</a> 
</li> <li> <a href="#preparator-data-processing-with-dase">Preparator : Data 
Processing With DASE</a> </li> <li> <a href="#algorithm-component">Algorithm 
Component</a> </li> <li> <a 
href="#serving-delivering-the-final-prediction">Serving: Delivering the Final 
Prediction</a> </li> <li> <a 
href="#evaluation-model-assessment-and-selection">Evaluation: Model Assessment 
and Selection</a> </li> <li> <a href="#engine-deployment">Engine De
 ployment</a> </li> </ul> </aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/demo/textclassification.html.md.erb";><img
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class="hidden-sm hidden xs"><ul><li><a href="#">Demo Tutorials</a><span 
class="spacer">&gt;</span></li><li><span class="last">Text Classification 
Engine Tutorial</span></li></ul></div><div id="page-title"><h1>Text 
Classification Engine Tutorial</h1></div></div><div class="content"> 
<p>(Updated for Text Classification Template version 3.1)</p><h2 
id='introduction' class='header-anchors'>Introduction</h2><p>In the real world, 
there are many applications that collect text as data. For example, spam 
detectors take email and header content to automatically determine what is or 
is not spam; applications can gauge the general sentiment in a geographical 
area by analyzing Twit
 ter data; and news articles can be automatically categorized based solely on 
the text content.There are a wide array of machine learning models you can use 
to create, or train, a predictive model to assign an incoming article, or 
query, to an existing category. Before you can use these techniques you must 
first transform the text data (in this case the set of news articles) into 
numeric vectors, or feature vectors, that can be used to train your 
model.</p><p>The purpose of this tutorial is to illustrate how you can go about 
doing this using PredictionIO&#39;s platform. The advantages of using this 
platform include: a dynamic engine that responds to queries in real-time; <a 
href="http://en.wikipedia.org/wiki/Separation_of_concerns";>separation of 
concerns</a>, which offers code re-use and maintainability, and distributed 
computing capabilities for scalability and efficiency. Moreover, it is easy to 
incorporate non-trivial data modeling tasks into the DASE architecture allowing 
Data Sc
 ientists to focus on tasks related to modeling. This tutorial will exemplify 
some of these ideas by guiding you through PredictionIO&#39;s <a 
href="/gallery/template-gallery/#natural-language-processing">text 
classification template</a>.</p><h2 id='prerequisites' 
class='header-anchors'>Prerequisites</h2><p>Before getting started, please make 
sure that you have the latest version of Apache PredictionIO <a 
href="http://predictionio.apache.org/install/";>installed</a>. We emphasize here 
that this is an engine template written in <strong>Scala</strong> and can be 
more generally thought of as an SBT project containing all the necessary 
components.</p><p>You should also download the engine template named Text 
Classification Engine that accompanies this tutorial by cloning the template 
repository:</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td class="code"><pre>git clone https://
 github.com/apache/predictionio-template-text-classifier.git &lt; Your new 
engine directory &gt;
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href="/"><span>Welcome to Apache PredictionIO®</span></a></li></ul></li><li 
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href="#"><span>Integrating with Your App</span
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href="/datacollection/batchimport/"><span>Importing Data in 
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Tools</span></a></li><li class="level-2"><a class="final" 
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Plugin</span></a></li></ul></li><li class="level-1
 "><a class="expandible" href="#"><span>Choosing an Algorithm</span></a><ul><li 
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href="/algorithm/switch/"><span>Switching to Another 
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href="/algorithm/multiple/"><span>Combining Multiple 
Algorithms</span></a></li><li class="level-2"><a class="final" 
href="/algorithm/custom/"><span>Adding Your Own 
Algorithms</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Tuning and Evaluation</span></a><ul><li class="level-2"><a 
class="final" href="/evaluation/"><span>Overview</span></a></li><li 
class="level-2"><a class="final" 
href="/evaluation/paramtuning/"><span>Hyperparameter Tuning</span></a></li><li 
class="level-2"><a class="final" 
href="/evaluation/evaluationdashboard/"><span>Evaluation 
Dashboard</span></a></li><li class="level-2"><a class="final" href="/evaluatio
 n/metricchoose/"><span>Choosing Evaluation Metrics</span></a></li><li 
class="level-2"><a class="final" href="/evaluation/metricbuild/"><span>Building 
Evaluation Metrics</span></a></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>System Architecture</span></a><ul><li 
class="level-2"><a class="final" href="/system/"><span>Architecture 
Overview</span></a></li><li class="level-2"><a class="final" 
href="/system/anotherdatastore/"><span>Using Another Data 
Store</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>PredictionIO® Official Templates</span></a><ul><li 
class="level-2"><a class="final" 
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class="level-3"><a class="final" 
href="/templates/recommendation/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/recommendation/dase/"><span>DASE</span></a></li
 ><li class="level-3"><a class="final" 
 >href="/templates/recommendation/evaluation/"><span>Evaluation 
 >Explained</span></a></li><li class="level-3"><a class="final" 
 >href="/templates/recommendation/how-to/"><span>How-To</span></a></li><li 
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 >href="/templates/recommendation/reading-custom-events/"><span>Read Custom 
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 >href="/templates/recommendation/customize-data-prep/"><span>Customize Data 
 >Preparator</span></a></li><li class="level-3"><a class="final" 
 >href="/templates/recommendation/customize-serving/"><span>Customize 
 >Serving</span></a></li><li class="level-3"><a class="final" 
 >href="/templates/recommendation/training-with-implicit-preference/"><span>Train
 > with Implicit Preference</span></a></li><li class="level-3"><a class="final" 
 >href="/templates/recommendation/blacklist-items/"><span>Filter Recommended 
 >Items by Blacklist in Query</span></a></li><li class="level-3"><a 
 >class="final" href="/templates/r
 ecommendation/batch-evaluator/"><span>Batch Persistable 
Evaluator</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>E-Commerce Recommendation</span></a><ul><li class="level-3"><a 
class="final" href="/templates/ecommercerecommendation/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
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 class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/train-with-rate-event/"><span>Train 
with Rate Event</span></a></li><li class="level-3"><a class="final" 
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Score</span></a></li></ul></li><li class="level-2"><a class="expandible" 
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 ick Start</span></a></li><li class="level-3"><a class="final" 
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class="level-3"><a class="final" 
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Event</span></a></li><li class="level-3"><a class="final" 
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for Users</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/recommended-user/"><span>Recommend 
Users</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>C
 lassification</span></a><ul><li class="level-3"><a class="final" 
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class="level-3"><a class="final" 
href="/templates/classification/add-algorithm/"><span>Use Alternative 
Algorithm</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/reading-custom-properties/"><span>Read Custom 
Properties</span></a></li></ul></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>Engine Template Gallery</span></a><ul><li 
class="level-2"><a class="final" 
href="/gallery/template-gallery/"><span>Browse</span></a></li><li 
class="level-2"><a class="final" 
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hidden-lg"><div id="breadcrumbs" class="hidden-sm hidden xs"><ul><li><a 
href="#">Demo Tutorials</a><span class="spacer">&gt;</span></li><li><span 
class="last">Text Classification Engine Tutorial</span></li></ul></div><div 
id="page-title"><h1>Text Classification Engine Tutorial</h1></div></div><div 
id="table-of-content-wrapper"><h5>On this page</h5><aside i
 d="table-of-contents"><ul> <li> <a href="#introduction">Introduction</a> </li> 
<li> <a href="#prerequisites">Prerequisites</a> </li> <li> <a 
href="#engine-overview">Engine Overview</a> </li> <li> <a 
href="#quick-start">Quick Start</a> </li> </ul> </li> <li> <a 
href="#detailed-explanation-of-dase">Detailed Explanation of DASE</a> <ul> <li> 
<a href="#importing-data">Importing Data</a> </li> <li> <a 
href="#data-source-reading-event-data">Data Source: Reading Event Data</a> 
</li> <li> <a href="#preparator-data-processing-with-dase">Preparator : Data 
Processing With DASE</a> </li> <li> <a href="#algorithm-component">Algorithm 
Component</a> </li> <li> <a 
href="#serving-delivering-the-final-prediction">Serving: Delivering the Final 
Prediction</a> </li> <li> <a 
href="#evaluation-model-assessment-and-selection">Evaluation: Model Assessment 
and Selection</a> </li> <li> <a href="#engine-deployment">Engine Deployment</a> 
</li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/a
 
pache/predictionio/tree/livedoc/docs/manual/source/demo/textclassification.html.md.erb"><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="breadcrumbs" 
class="hidden-sm hidden xs"><ul><li><a href="#">Demo Tutorials</a><span 
class="spacer">&gt;</span></li><li><span class="last">Text Classification 
Engine Tutorial</span></li></ul></div><div id="page-title"><h1>Text 
Classification Engine Tutorial</h1></div></div><div class="content"> 
<p>(Updated for Text Classification Template version 3.1)</p><h2 
id='introduction' class='header-anchors'>Introduction</h2><p>In the real world, 
there are many applications that collect text as data. For example, spam 
detectors take email and header content to automatically determine what is or 
is not spam; applications can gauge the general sentiment in a geographical 
area by analyzing Twitter data; and news articles can be automatically 
categorized based solely on the text c
 ontent.There are a wide array of machine learning models you can use to 
create, or train, a predictive model to assign an incoming article, or query, 
to an existing category. Before you can use these techniques you must first 
transform the text data (in this case the set of news articles) into numeric 
vectors, or feature vectors, that can be used to train your model.</p><p>The 
purpose of this tutorial is to illustrate how you can go about doing this using 
PredictionIO&#39;s platform. The advantages of using this platform include: a 
dynamic engine that responds to queries in real-time; <a 
href="http://en.wikipedia.org/wiki/Separation_of_concerns";>separation of 
concerns</a>, which offers code re-use and maintainability, and distributed 
computing capabilities for scalability and efficiency. Moreover, it is easy to 
incorporate non-trivial data modeling tasks into the DASE architecture allowing 
Data Scientists to focus on tasks related to modeling. This tutorial will 
exemplify some of th
 ese ideas by guiding you through PredictionIO&#39;s <a 
href="/gallery/template-gallery/#natural-language-processing">text 
classification template</a>.</p><h2 id='prerequisites' 
class='header-anchors'>Prerequisites</h2><p>Before getting started, please make 
sure that you have the latest version of Apache PredictionIO <a 
href="http://predictionio.apache.org/install/";>installed</a>. We emphasize here 
that this is an engine template written in <strong>Scala</strong> and can be 
more generally thought of as an SBT project containing all the necessary 
components.</p><p>You should also download the engine template named Text 
Classification Engine that accompanies this tutorial by cloning the template 
repository:</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td class="code"><pre>git clone 
https://github.com/apache/predictionio-template-text-classifier.git &lt; Your 
new engine direct
 ory &gt;
 </pre></td></tr></tbody></table> </div> <h2 id='engine-overview' 
class='header-anchors'>Engine Overview</h2><p>The engine follows the DASE 
architecture which we briefly review here. As a user, you are tasked with 
collecting data for your web or application, and importing it into 
PredictionIO&#39;s Event Server. Once the data is in the server, it can be read 
and processed by the engine via the Data Source and Preparation components, 
respectively. The Algorithm component uses the processed, or prepared, data to 
train a set of predictive models. Once you have trained these models, you are 
ready to deploy your engine and respond to real-time queries via the Serving 
component which combines the results from different fitted models. The 
Evaluation component is used to compute an appropriate metric to test the 
performance of a fitted model, as well as aid in the tuning of model hyper 
parameters.</p><p>This engine template is meant to handle text classification 
which means you will be worki
 ng with text data. This means that a query, or newly observed documents, will 
be of the form:</p><p><code>{text : String}</code>.</p><p>In the running 
example, a query would be an incoming news article. Once the engine is deployed 
it can process the query, and then return a Predicted Result of the 
form</p><p><code>{category : String, confidence : Double}</code>.</p><p>Here 
category is the model&#39;s class assignment for this new text document (i.e. 
the best guess for this article&#39;s categorization), and confidence, a value 
between 0 and 1 representing your confidence in the category prediction (0 
meaning you have no confidence in the prediction). The Actual Result is of the 
form</p><p><code>{category : String}</code>.</p><p>This is used in the 
evaluation stage when estimating the performance of your predictive model (how 
well does the model predict categories). Please refer to the <a 
href="https://predictionio.apache.org/customize/";>following tutorial</a> for a 
more detailed exp
 lanation of how your engine will interact with your web application, as well 
as an in depth-overview of DASE.</p><h2 id='quick-start' 
class='header-anchors'>Quick Start</h2><p>This is a quick start guide in case 
you want to start using the engine right away. Sample email data for spam 
classification will be used. For more detailed information, read the subsequent 
sections.</p><h3 id='1.-create-a-new-application.' class='header-anchors'>1. 
Create a new application.</h3><p>After the application is created, you will be 
given an access key and application ID for the application.</p><div 
class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td 
class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td 
class="code"><pre><span class="gp">$ </span>pio app new MyTextApp
 </pre></td></tr></tbody></table> </div> <h3 id='2.-import-the-tutorial-data.' 
class='header-anchors'>2. Import the tutorial data.</h3><p>There are three 
different data sets available, each giving a different use case for this 
engine. Please refer to the <strong>Data Source: Reading Event Data</strong> 
section to see how to appropriate modify the <code>DataSource</code> class for 
use with each respective data set. The default data set is an e-mail spam data 
set.</p><p>These data sets have already been processed and are ready for <a 
href="/datacollection/batchimport/">batch import</a>. Replace <code>***</code> 
with your actual application ID.</p><div class="highlight shell"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1
 2

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