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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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| <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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| <a href="docs/latest/group__grp__linreg.html#examples"> | |
| <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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| <a href="docs/latest/group__grp__lda.html#examples"> | |
| <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> | |
| <p class="body"> | |
| 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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| <div class="post-206 post type-post status-publish format-standard hentry category-use-case span4 simple-box "> | |
| <a href="docs/latest/group__grp__logreg.html#examples"> | |
| <h4 class="title pointer point-right">Logistic Regression</h4> | |
| <p class="body"> | |
| Logistic regression is used to predict a binary outcome of a dependent variable from one or more explanatory independent variables. | |
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| <a href="docs/latest/group__grp__elasticnet.html#examples"> | |
| <h4 class="title pointer point-right">Elastic Net Regularization</h4> | |
| <p class="body"> | |
| 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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| <a href="docs/latest/group__grp__pca__train.html#examples"> | |
| <h4 class="title pointer point-right">Principal Component Analysis</h4> | |
| <p class="body"> | |
| 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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| <a href="docs/latest/group__grp__assoc__rules.html#examples"> | |
| <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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| <a href="docs/latest/group__grp__kmeans.html#examples"> | |
| <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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| <div class="post-216 post type-post status-publish format-standard hentry category-use-case span4 simple-box "> | |
| <a href="docs/latest/group__grp__sssp.html#examples"> | |
| <h4 class="title pointer point-right">Shortest Path in a Graph</h4> | |
| <p class="body"> | |
| 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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