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hidden-lg"><div id="page-title"><h1>Demos</h1></div></div><div 
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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 
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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 
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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-superv
 ised-learning">Introduction 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? <stron
 g>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 o
 therwise, 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 a
 ny 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 specific 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 
l
 earning called <strong>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.</
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