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 class="level-2"><a class="final" 
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 aluation/"><span>Evaluation Explained</span></a></li><li class="level-3"><a 
class="final" 
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class="level-3"><a class="final" 
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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" 
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with Implicit Preference</span></a></li><li class="level-3"><a class="final" 
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Items by Blacklist in Query</span></a></li><li class="level-3"><a class="final" 
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Evaluator</span>
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href="/templates/ecommercerecommendation/how-to/"><span>How-To</span></a></li><li
 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" 
href="/templates/ecommercerecommendation/adjust-score/"><span>Adjust 
Score</span></a></li></ul></li><li class="level-2"><a class="expandible" 
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class="final" href="/templates/similarproduct/quickstart/"><span>Quick 
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class="level-3"><a class="final" 
href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple 
Events and Multiple Algorithms</span></a></li><li class="level-3"><a 
class="final" 
href="/templates/similarproduct/return-item-properties/"><span>Returns Item 
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href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events 
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>Classification</span></a><ul><li class="level-3"><a 
class="final" href="/
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href="/templates/classification/dase/"><span>DASE</span></a></li><li 
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href="/templates/classification/how-to/"><span>How-To</span></a></li><li 
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IDEA</span></a></li><li class="level-2"><a class="final" 
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class="level-1"><a class="expandible" href="#"><span>Apache Software 
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 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
hidden-lg"><div id="page-title"><h1>Demos</h1></div></div><div 
id="table-of-content-wrapper"><h5>On this page</h5><aside 
id="table-of-contents"><ul> <li> <a href="#current-demos">Current Demos</a> 
</li> <li> <a href="#contribute">Contribute</a> </li> </ul> </aside><hr/><a 
id="edit-page-link" href="https://github.com/apache/incubator-predi
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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>Demos</h1></div></div><div class="content"> <h2 
id='current-demos' class='header-anchors'>Current Demos</h2><p><a 
href="/demo/tapster/">Tapster</a> - A Tinder like Rails application using 
Apache PredictionIO (incubating).</p><h2 id='contribute' 
class='header-anchors'>Contribute</h2><p>Interested in writing a demo? Contact 
us at <a href="mailto:[email protected]";><a 
href="mailto:[email protected]";>[email protected]</a></a>.</p></div></div></div></div><footer><div
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 >PredictionIO is an effort undergoing incubation at The Apache Software 
 >Foundation (ASF), sponsored by the Apache Incubator. Incubation is required 
 >of all newly accepted projects until a further review indicates that the 
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 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
hidden-lg"><div id="page-title"><h1>Machine Learning With 
PredictionIO</h1></div></div><div id="table-of-content-wrapper"><h5>On this 
page</h5><aside id="table-of-contents"><ul> <li> <a 
href="#introduction-to-supervised-learning">Intr
 oduction to Supervised Learning</a> </li> <li> <a 
href="#predictionio-and-supervised-learning">PredictionIO and Supervised 
Learning</a> </li> </ul> </aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/demo/supervisedlearning.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>Machine 
Learning With PredictionIO</h1></div></div><div class="content"> <p>This guide 
is designed to give developers a brief introduction to fundamental concepts in 
machine learning, as well as an explanation of how these concept tie into 
PredictionIO&#39;s engine development platform. This particular guide will 
largely deal with giving some</p><h2 id='introduction-to-supervised-learning' 
class='header-anchors'>Introduction to Supervised Learning</h2><p>The first 
question we must ask is: what is machine learning? <strong>Machine learning<
 /strong> is the field of study at the intersection of computer science, 
engineering, mathematics, and statistics which seeks to discover or infer 
patterns hidden within a set of observations, which we call our data. Some 
examples of problems that machine learning seeks to solve are:</p> <ul> 
<li>Predict whether a patient has breast cancer based on their mammogram 
results.</li> <li>Predict whether an e-mail is spam or not based on the 
e-mail&#39;s content.</li> <li>Predict today&#39;s temperature based on climate 
variables collected for the previous week.</li> </ul> <h3 
id='thinking-about-data' class='header-anchors'>Thinking About Data</h3><p>In 
the latter examples, we are trying to predict an outcome \(Y\), or 
<strong>response</strong>, based on some recorded or observed variables \(X\), 
or <strong>features</strong>. For example: in the third problem each 
observation is a patient, the response variable \(Y\) is equal to 1 if this 
patient has breast cancer and 0 otherwise, and \(X\)
  represents the mammogram results.</p><p>When we say we want to predict \(Y\) 
using \(X\), we are trying to answer the question: how does a response \(Y\) 
depend on a set of features \(X\) affect the response \(Y\)? To do this we need 
a set of observations, which we call our <strong>training data</strong>, 
consisting of observations for which we have observed both \(Y\) and \(X\), in 
order to make inference about this relationship.</p><h3 
id='different-types-of-supervised-learning-problems' 
class='header-anchors'>Different Types of Supervised Learning 
Problems</h3><p>Note that in the first two examples, the outcome \(Y\) can only 
take on two values (1 : cancer/spam, 0: no cancer/ no spam). Whenever the 
outcome variable \(Y\) denotes a label associated to a particular group of 
observations (i.e. cancer group), the <strong>supervised learning</strong> 
problem is also called a <strong>classification</strong> problem. In the third 
example, however, \(Y\) can take on any numerical value 
 since it denotes some temperature reading (i.e. 25.143, 25.14233, 32.0). These 
types of supervised learning problems are also called 
<strong>regression</strong> problems.</p><h3 id='training-a-predictive-model' 
class='header-anchors'>Training a Predictive Model</h3><p>A predictive model 
should be thought of as a function \(f\) that takes as input a set of features, 
and outputs a predicted outcome (i.e. \(f(X) = Y\)). The phrase 
<strong>training a model</strong> simply refers to the process of using the 
training data to estimate such a function. </p><h2 
id='predictionio-and-supervised-learning' class='header-anchors'>PredictionIO 
and Supervised Learning</h2><p>Machine learning methods generally assume that 
our observation responses and features are numeric vectors. We will say that 
observations in this format are in <strong>standard form</strong>. However, 
when you are working with real-life data this will often not be the case. The 
data will often be formatted in a manner that is sp
 ecific to the application&#39;s needs. As an example, let&#39;s suppose our 
application is <a href="http://stackoverflow.com";>StackOverFlow</a>. The data 
we want to analyze are questions, and we want to predict based on a 
question&#39;s content whether or not it is related to 
Scala.</p><p><strong>Self-check:</strong> Is this a classification or 
regression problem?</p><h3 id='thinking-about-data-with-predictionio' 
class='header-anchors'>Thinking About Data With 
PredictionIO</h3><p>PredictionIO&#39;s predictive engine development platform 
allows you to easily incorporate observations that are not in standard form. 
Continuing with our example, we can import the observations, or StackOverFlow 
questions, into <a href="/datacollection/">PredictionIO&#39;s Event Server</a> 
as events with the following properties:</p><p><code>properties = {question : 
String, topic : String}</code></p><p>The value <code>question</code> is the 
actual question stored as a <code>String</code>, and topic is also
  a string equal to either <code>&quot;Scala&quot;</code> or 
<code>&quot;Other&quot;</code>. Our outcome here is <code>topic</code>, and 
<code>question</code> will provide a source for extracting features. That is, 
we will be using <code>question</code> to predict the outcome 
<code>topic</code>.</p><p>Once the observations are loaded as events into the 
Event Server, the engine&#39;s <a href="/customize/">Data Source</a> component 
is able to read them, which allows you to treat them as objects in a Scala 
project. The engine&#39;s Preparator component is in charge of converting these 
observations into standard form. To do this, we can first map the topic values 
as follows:</p><p><code>Map(&quot;Other&quot; -&gt; 0, &quot;Scala&quot; -&gt; 
1)</code>.</p><p>We can then vectorize the observation&#39;s associated 
question text to obtain a numeric feature vector for each of our observations. 
This text vectorization procedure is an example of a general concept in machine 
learning called <str
 ong>feature extraction</strong>. After performing these transformations of our 
observations, they are now in standard form and can be used for training a 
large quantity of machine learning models.</p><h3 
id='training-the-model-with-predictionio' class='header-anchors'>Training the 
Model With PredictionIO</h3><p>The Algorithm engine component serves two 
purposes: outputting a predictive model \(f\) and using this to predict the 
outcome variable. Here \(f\) takes as input a vectorized question and outputs 
either 0 or 1. However, our <code>Query</code> input will be again a question, 
and our <code>PredictedResult</code> the topic associated to the predicted 
label (0 or 1):</p><p><code>Query = {question : String}</code> 
<code>PredictedResult = {topic : String}</code></p><p>With PredictionIO&#39;s 
engine development platform, you can easily automate the vectorization of the 
Query question, as well as mapping the predicted label to the appropriate topic 
output format.</p></div></div></div
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