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<h1 class="h2">Documentation</h1>
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<h3><a class="pointer point-right" href="docs/latest/index.html">Latest User Guide</a></h3>
<p class="body">The primary documentation reference material providing detailed information on the functions and algorithms within MADlib as well as background theory and references into the literature.</p>
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<option value="docs/v1.15.1/index.html">MADlib v1.15.1</option>
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<h3><a class="pointer point-right" href="https://cwiki.apache.org/confluence/display/MADLIB/Installation+Guide">Installation Guide</a></h3>
<p class="body">Information on initial installation and deployment of MADlib into a database instance.</p>
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<h3><a class="pointer point-right" href="https://cwiki.apache.org/confluence/display/MADLIB/Quick+Start+Guide+for+Users">Quick Start Guide for Users</a></h3>
<p class="body">Introduction to themes and concepts in MADlib. The guide walks the user through an initial data load, training a model, inspecting a model, and scoring a model.</p>
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<h3><a class="pointer point-right" href="https://cwiki.apache.org/confluence/display/MADLIB/Quick+Start+Guide+for+Developers">Quick Start Guide for Developers</a></h3>
<p class="body">For developers who are interested in contributing to MADlib. Includes instructions for an available Docker image with necessary dependencies to compile and test MADlib.</p>
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<h3><a class="pointer point-right" href="https://github.com/apache/madlib-site/tree/asf-site/community-artifacts">Jupyter Notebooks for Getting Started</a></h3>
<p class="body">Includes many commonly used algorithms by data scientists.</p>
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<p class="body">Additional material for individuals looking to contribute to the project is available on our community portal.</p>
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<h2>Example Use Cases</h2>
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<h4 class="title pointer point-right">Linear Regression</h4>
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Linear regression is used to model the linear relationship of a scalar dependent variable to one or more explanatory independent variables.
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<h4 class="title pointer point-right">Latent Dirichlet Allocation</h4>
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Latent Dirichlet Allocation is a topic modeling function used to identify recurring themes in a large document corpus.
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<h4 class="title pointer point-right">Summary</h4>
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The summary function provides summary statistics for any data table. These statistics include: number of distinct values, number of missing values, mean, variance, min, max, most frequent values, quantiles, etc.
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<h4 class="title pointer point-right">Logistic Regression</h4>
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Logistic regression is used to predict a binary outcome of a dependent variable from one or more explanatory independent variables.
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<h4 class="title pointer point-right">Elastic Net Regularization</h4>
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Elastic Net regularization is a technique that can be applied to either linear or logistic regression to build a more robust model, in the event of large numbers of explanatory independent variables.
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<h4 class="title pointer point-right">Principal Component Analysis</h4>
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Pricipal Component Analysis is a dimensional reduction technique that can be used to transform a high dimensional space into a lower dimensional space.
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<h4 class="title pointer point-right">Apriori</h4>
<p class="body">
Apriori is a technique for evaluating frequent item-sets, which allows analysis of what events tend to occur together. For example, which items do customers frequently purchase together in a single transaction?
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<h4 class="title pointer point-right">k-Means</h4>
<p class="body">
k-Means is a clustering method used to identify regions of similarity within a dataset. It can be used for many types of analysis including customer segmentation.
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<h4 class="title pointer point-right">Shortest Path in a Graph</h4>
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Finds a path from a source vertex to every other vertex in the graph. Example uses include vehicle routing/navigation, degrees of separation in a social network and minimum delay path in a telecommunications network.
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