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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">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/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'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's content.</li> <li>Predict today'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 sa
y 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 specific to the
application's needs. As an exampl
e, let'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'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'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'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>"Scala"</
code> or <code>"Other"</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'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'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("Other" -> 0,
"Scala" -> 1)</code>.</p><p>We can then vectorize the
observation'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 <strong>feature
extraction</strong>. After performing t
hese 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'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></div><footer><div class="container"><div
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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">Introduction to Supervised
Learning</a> </li> <l
i> <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/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'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's content.</li> <li>Predict today'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 w
e 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 specific to the
application's needs. As an example,
let'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'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'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'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>"Scala"</cod
e> or <code>"Other"</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'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'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("Other" -> 0, "Scala" ->
1)</code>.</p><p>We can then vectorize the observation'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 <strong>feature extraction</strong>. After performing thes
e 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'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></div><footer><div class="container"><div
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