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 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
hidden-lg"><div id="page-title"><h1>Modeling Workflow and 
DASE</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#training-the-model">Training The Model</a> </
 li> </ul> </aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/machinelearning/modelingworkflow.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="page-title"><h1>Modeling 
Workflow and DASE</h1></div></div><div class="content"> <p>In addition to the 
DASE components, we also introduce the Data Model and Training Model 
abstractions. The Data Model abstraction refers to the set of Scala classes 
dealing with the implementation of modeling choices relating to feature 
<strong>extraction</strong>, <strong>preparation</strong>, and/or 
<strong>selection</strong>. For this illustration, this only includes the 
vectorization of text and t.f.-i.d.f. processing which is entirely implemented 
in the PreparedData class. The Training Model abstraction refers to any set of 
classes that individually take in a set of feature observations and outp
 ut a predictive model. This predictive model is leveraged by the Algorithm 
component to produce prediction results to queries in real-time. In the engine 
template, this abstraction is implemented in the NBModel class. <strong>Please 
note that these are conceptual abstractions that are designed to make engine 
development easier by decoupling class functionality.</strong> Keeping these 
abstractions in mind will help you in the future with debugging your code, and 
also make it easier to incorporate different modeling ideas into your 
engine.</p><p>The figure below shows a graphical representation of the engine 
architecture just described, as well as its interactions with your web/app and 
a provided Event Server:</p><p><img alt="Engine Overview" 
src="/images/demo/text_classification_template/engine_overview-27e09a89.png"/></p><h2
 id='training-the-model' class='header-anchors'>Training The Model</h2><p>This 
section will guide you through the two Training Model implementations that come 
wi
 th this engine template. Recall that the Training Model abstraction refers to 
an arbitrary set Scala Class that outputs a predictive model (i.e. implements 
some method that can be used for prediction). The general problem this engine 
template is tackling is text classification, so that our Training Model 
abstraction domain is restricted to implementations producing classifiers. In 
particular, the classification model that is implemented in this engine 
template is based on Multinomial Naive Bayes using t.f.-i.d.f. vectorized 
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hidden-lg"><div id="page-title"><h1>Modeling Workflow and 
DASE</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#training-the-model">Training The Model</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" href="
 
https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/machinelearning/modelingworkflow.html.md";><img
 src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div 
class="content-header hidden-sm hidden-xs"><div id="page-title"><h1>Modeling 
Workflow and DASE</h1></div></div><div class="content"> <p>In addition to the 
DASE components, we also introduce the Data Model and Training Model 
abstractions. The Data Model abstraction refers to the set of Scala classes 
dealing with the implementation of modeling choices relating to feature 
<strong>extraction</strong>, <strong>preparation</strong>, and/or 
<strong>selection</strong>. For this illustration, this only includes the 
vectorization of text and t.f.-i.d.f. processing which is entirely implemented 
in the PreparedData class. The Training Model abstraction refers to any set of 
classes that individually take in a set of feature observations and output a 
predictive model. This predictive model is leve
 raged by the Algorithm component to produce prediction results to queries in 
real-time. In the engine template, this abstraction is implemented in the 
NBModel class. <strong>Please note that these are conceptual abstractions that 
are designed to make engine development easier by decoupling class 
functionality.</strong> Keeping these abstractions in mind will help you in the 
future with debugging your code, and also make it easier to incorporate 
different modeling ideas into your engine.</p><p>The figure below shows a 
graphical representation of the engine architecture just described, as well as 
its interactions with your web/app and a provided Event Server:</p><p><img 
alt="Engine Overview" 
src="/images/demo/text_classification_template/engine_overview-27e09a89.png"/></p><h2
 id='training-the-model' class='header-anchors'>Training The Model</h2><p>This 
section will guide you through the two Training Model implementations that come 
with this engine template. Recall that the Training Mo
 del abstraction refers to an arbitrary set Scala Class that outputs a 
predictive model (i.e. implements some method that can be used for prediction). 
The general problem this engine template is tackling is text classification, so 
that our Training Model abstraction domain is restricted to implementations 
producing classifiers. In particular, the classification model that is 
implemented in this engine template is based on Multinomial Naive Bayes using 
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