http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/765e178c/archived/install-vagrant/index.html ---------------------------------------------------------------------- diff --git a/archived/install-vagrant/index.html b/archived/install-vagrant/index.html new file mode 100644 index 0000000..ce838d2 --- /dev/null +++ b/archived/install-vagrant/index.html @@ -0,0 +1,49 @@ +<!DOCTYPE html><html><head><title>Installing PredictionIO with Vagrant (VirtualBox)</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta class="swiftype" name="title" data-type="string" content="Installing PredictionIO with Vagrant (VirtualBox)"/><link rel="canonical" href="https://predictionio.apache.org/archived/install-vagrant/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link 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href="https://www.apache.org/foundation/sponsorship.html";><span>Sponsorship</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/thanks.html";><span>Thanks</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/security/";><span>Security</span></a></li></ul></li></ul></nav></div><div class="col-md-9 col-sm-12"><div class="content-header hidden-md hidden-lg"><div id="page-title"><h1>Installing PredictionIO with Vagrant (VirtualBox)</h1></div></div><div id="table-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a href="#install-virtualbox">Install VirtualBox</a> </li> <li> <a href="#install-vagrant">Install Vagrant</a> </li> <li> <a href="#bring-up-predictoinio-vm-with-vagrant">Bring up PredictoinIO VM with Vagrant</a> </li> <li> <a href="#using-the-predictionio-vm">Using the PredictionIO VM</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/archived/install-vagrant.html.md.erb";><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>Installing PredictionIO with Vagrant (VirtualBox)</h1></div></div><div class="content"> <div class="alert-message warning"><p>Running PredictionIO with Vagrant is intended for the purposes of simple tests in an isolated environment. Due to resource limitation and overhead of virtual machine (VM), it runs much more slowly or may encounter memory issue. We recommend using Linux or Mac machine for serious usage.</p></div><h2 id='install-virtualbox' class='header-anchors'>Install VirtualBox</h2><p>If you don't hav e VirtualBox installed, please follow the instructions in the <a href="https://www.virtualbox.org/wiki/Downloads";>VirtualBox site</a> to download and install it. After installation is done, you don't need to setup anything in Virtual Box. Vagrant will do it for you later.</p><h2 id='install-vagrant' class='header-anchors'>Install Vagrant</h2><p>If you don't have Vagrant installed, please follow the instructions in the the <a href="https://www.vagrantup.com/downloads.html";>Vagrant site</a> to download and install it.</p><h2 id='bring-up-predictoinio-vm-with-vagrant' class='header-anchors'>Bring up PredictoinIO VM with Vagrant</h2><p>Get the latest vagrant setup from github and make sure in master branch:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2 +3</pre></td><td class="code"><pre><span class="gp">$ </span>git clone https://github.com/PredictionIO/PredictionIO-Vagrant.git +<span class="gp">$ </span><span class="nb">cd </span>PredictionIO-Vagrant/ +<span class="gp">$ </span>git checkout master +</pre></td></tr></tbody></table> </div> <p>Inside the directory <code>PredictionIO-Vagrant/</code>, you will find a file named <code>Vagrantfile</code> which is the configuration file used by Vagrant to setup the VM. You may modify this file if you want to change the VM configuration.</p><p>For example, if you want to change the memory of the VM, you can locate the following line in the <code>Vagrantfile</code> and change the value passed to the <code>memory</code> parameter (default is 2048MB):</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre> v.customize <span class="o">[</span><span class="s2">"modifyvm"</span>, :id, <span class="s2">"--cpuexecutioncap"</span>, <span class="s2">"90"</span>, <span class="s2">"--memory"</span>, <span class="s2">"2048"</span><span class="o">]</span> +</pre></td></tr></tbody></table> </div> <p>In the directory <code>PredictionIO-Vagrant/</code>, bring up PredictionIO VM by running:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span>vagrant up +</pre></td></tr></tbody></table> </div> <div class="alert-message info"><p>When you run <code>vagrant up</code> for the first time, it will download the base box ubuntu/trusty64 if you don't have it. Then it will also install all necessary libraries and setup PredictionIO in the virtual machine.</p></div><p>When it finishes successfully, you should see something like the following:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11</pre></td><td class="code"><pre><span class="gp">==> </span>default: Installation <span class="k">done</span>! +<span class="gp">==> </span>default: -------------------------------------------------------------------------------- +<span class="gp">==> </span>default: Installation of PredictionIO 0.13.0 <span class="nb">complete</span>! +<span class="gp">==> </span>default: IMPORTANT: You still have to start PredictionIO and dependencies manually: +<span class="gp">==> </span>default: Run: <span class="s1">'pio-start-all'</span> +<span class="gp">==> </span>default: Check the status with: <span class="s1">'pio status'</span> +<span class="gp">==> </span>default: Use: <span class="s1">'pio [train|deploy|...]'</span> commands +<span class="gp">==> </span>default: Please report any problems to: [email protected] +<span class="gp">==> </span>default: Documentation at: http://predictionio.apache.org +<span class="gp">==> </span>default: -------------------------------------------------------------------------------- +<span class="gp">==> </span>default: Finish PredictionIO installation. +</pre></td></tr></tbody></table> </div> <p>That's it! Now you have a PredictionIO VM running!</p><p>Please see the following notes regarding how to use PredictionIO VM with vagrant.</p><h2 id='using-the-predictionio-vm' class='header-anchors'>Using the PredictionIO VM</h2><h3 id='login-to-the-vm' class='header-anchors'>Login to the VM</h3><p>You could ssh to the VM by running the following from your host machine in the same directory where you run <code>vagrant up</code> (i.e. PredictionIO-Vagrant/)</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span>vagrant ssh +</pre></td></tr></tbody></table> </div> <p>Then your console prompt becomes something like the following, which means you have logged into the VM:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre>vagrant@vagrant-ubuntu-trusty-64:~<span class="err">$</span> +</pre></td></tr></tbody></table> </div> <p>One you've logged into the VM, you can proceed to <a href="/start/download">Choosing an Engine Template</a> or continue the QuickStart of the Engine template you have chosen.</p><h3 id='shutdown-and-bring-up-predictionio-vm-again' class='header-anchors'>Shutdown and bring up PredictionIO VM again</h3><p>When you are not using PredictionIO VM, you should shut down VM properly, by running the following <strong>in the host machine</strong> (not inside VM):</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span>vagrant halt +</pre></td></tr></tbody></table> </div> <div class="alert-message warning"><p>If you didn't shut down VM properly or you ran <code>vagrant suspend</code>, the VM may go to suspend state. HBase may not be running properly next time when you run <code>vagrant up.</code> In this case, you can always run <code>vagrant halt</code> to do a clean shutdown first before run <code>vagrant up</code> again.</p></div><p>Then you can run <code>vagrant up</code> again later to bring up the PredicitonIO VM again.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span>vagrant up +</pre></td></tr></tbody></table> </div> <p>When it's ready, you should see the following:</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1 +2 +3 +4 +5 +6</pre></td><td class="code"><pre><span class="gp">==> </span>default: -------------------------------------------------------------------------------- +<span class="gp">==> </span>default: PredictionIO VM is up! +<span class="gp">==> </span>default: You could run <span class="s1">'pio status'</span> inside VM <span class="o">(</span><span class="s1">'vagrant ssh'</span> to VM first<span class="o">)</span> to confirm <span class="k">if </span>PredictionIO is ready. +<span class="gp">==> </span>default: IMPORTANT: You still have to start the eventserver manually <span class="o">(</span>inside VM<span class="o">)</span>: +<span class="gp">==> </span>default: Run: <span class="s1">'pio eventserver'</span> +<span class="gp">==> </span>default: -------------------------------------------------------------------------------- +</pre></td></tr></tbody></table> </div> </div></div></div></div><footer><div class="container"><div class="seperator"></div><div class="row"><div class="col-md-6 footer-link-column"><div class="footer-link-column-row"><h4>Community</h4><ul><li><a href="//predictionio.apache.org/install/" target="blank">Download</a></li><li><a href="//predictionio.apache.org/" target="blank">Docs</a></li><li><a href="//github.com/apache/predictionio" target="blank">GitHub</a></li><li><a href="mailto:[email protected]"; target="blank">Subscribe to User Mailing List</a></li><li><a href="//stackoverflow.com/questions/tagged/predictionio" target="blank">Stackoverflow</a></li></ul></div></div><div class="col-md-6 footer-link-column"><div class="footer-link-column-row"><h4>Contribute</h4><ul><li><a href="//predictionio.apache.org/community/contribute-code/" target="blank">Contribute</a></li><li><a href="//github.com/apache/predictionio" target="blank">Source Code</a></li><li><a href="// issues.apache.org/jira/browse/PIO" target="blank">Bug Tracker</a></li><li><a href="mailto:[email protected]"; target="blank">Subscribe to Development Mailing List</a></li></ul></div></div></div><div class="row"><div class="col-md-12 footer-link-column"><p>Apache PredictionIO, PredictionIO, Apache, the Apache feather logo, and the Apache PredictionIO project logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.</p><p>All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p></div></div></div><div id="footer-bottom"><div class="container"><div class="row"><div class="col-md-12"><div id="footer-logo-wrapper"><img alt="PredictionIO" src="/images/logos/logo-white-d1e9c6e6.png"/><span>®</span></div><div id="social-icons-wrapper"><a class="github-button" href="https://github.com/apache/predictionio"; data-icon="octicon-star" data-show-count="true" aria-labe l="Star apache/predictionio on GitHub">Star</a> <a class="github-button" href="https://github.com/apache/predictionio/fork"; data-icon="octicon-repo-forked" data-show-count="true" aria-label="Fork apache/predictionio on GitHub">Fork</a> <script id="github-bjs" async="" defer="" src="https://buttons.github.io/buttons.js";></script><a href="https://twitter.com/predictionio"; target="blank"><img alt="PredictionIO on Twitter" src="/images/icons/twitter-ea9dc152.png"/></a> <a href="https://www.facebook.com/predictionio"; target="blank"><img alt="PredictionIO on Facebook" src="/images/icons/facebook-5c57939c.png"/></a> </div></div></div></div></div></footer></div><script>(function(w,d,t,u,n,s,e){w['SwiftypeObject']=n;w[n]=w[n]||function(){ +(w[n].q=w[n].q||[]).push(arguments);};s=d.createElement(t); +e=d.getElementsByTagName(t)[0];s.async=1;s.src=u;e.parentNode.insertBefore(s,e); +})(window,document,'script','//s.swiftypecdn.com/install/v1/st.js','_st'); + +_st('install','HaUfpXXV87xoB_zzCQ45');</script><script src="/javascripts/application-d943a254.js"></script></body></html> \ No newline at end of file
http://git-wip-us.apache.org/repos/asf/predictionio-site/blob/765e178c/archived/supervisedlearning/index.html ---------------------------------------------------------------------- diff --git a/archived/supervisedlearning/index.html b/archived/supervisedlearning/index.html new file mode 100644 index 0000000..8e3752d --- /dev/null +++ b/archived/supervisedlearning/index.html @@ -0,0 +1,6 @@ +<!DOCTYPE html><html><head><title>Machine Learning With PredictionIO</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta class="swiftype" name="title" data-type="string" content="Machine Learning With PredictionIO"/><link rel="canonical" href="https://predictionio.apache.org/archived/supervisedlearning/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link 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id="pill-wrapper"><a class="pill left" href="/gallery/template-gallery">TEMPLATES</a> <a class="pill right" href="//github.com/apache/predictionio/">OPEN SOURCE</a></div></div><img class="mobile-search-bar-toggler hidden-md hidden-lg" src="/images/icons/search-glass-704bd4ff.png"/></div></div></div></header><div id="search-bar-row-wrapper"><div class="container-fluid" id="search-bar-row"><div class="row"><div class="col-md-9 c ol-sm-11 col-xs-11"><div class="hidden-md hidden-lg" id="mobile-page-heading-wrapper"><p>PredictionIO Docs</p><h4>Machine Learning With PredictionIO</h4></div><h4 class="hidden-sm hidden-xs">PredictionIO Docs</h4></div><div class="col-md-3 col-sm-1 col-xs-1 hidden-md hidden-lg"><img id="left-menu-indicator" src="/images/icons/down-arrow-dfe9f7fe.png"/></div><div class="col-md-3 col-sm-12 col-xs-12 swiftype-wrapper"><div class="swiftype"><form class="search-form"><img class="search-box-toggler hidden-xs hidden-sm" src="/images/icons/search-glass-704bd4ff.png"/><div class="search-box"><img src="/images/icons/search-glass-704bd4ff.png"/><input type="text" id="st-search-input" class="st-search-input" placeholder="Search Doc..."/></div><img class="swiftype-row-hider hidden-md hidden-lg" src="/images/icons/drawer-toggle-active-fcbef12a.png"/></form></div></div><div class="mobile-left-menu-toggler hidden-md hidden-lg"></div></div></div></div><div id="page" class="container-fluid"><div clas s="row"><div id="left-menu-wrapper" class="col-md-3"><nav id="nav-main"><ul><li class="level-1"><a class="expandible" href="/"><span>Apache PredictionIO® Documentation</span></a><ul><li class="level-2"><a class="final" href="/"><span>Welcome to Apache PredictionIO®</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Getting Started</span></a><ul><li class="level-2"><a class="final" href="/start/"><span>A Quick Intro</span></a></li><li class="level-2"><a class="final" href="/install/"><span>Installing Apache PredictionIO</span></a></li><li class="level-2"><a class="final" href="/start/download/"><span>Downloading an Engine Template</span></a></li><li class="level-2"><a class="final" href="/start/deploy/"><span>Deploying Your First Engine</span></a></li><li class="level-2"><a class="final" href="/start/customize/"><span>Customizing the Engine</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Integrating with Your App</spa n></a><ul><li class="level-2"><a class="final" href="/appintegration/"><span>App Integration Overview</span></a></li><li class="level-2"><a class="expandible" href="/sdk/"><span>List of SDKs</span></a><ul><li class="level-3"><a class="final" href="/sdk/java/"><span>Java & Android SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/php/"><span>PHP SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/python/"><span>Python SDK</span></a></li><li class="level-3"><a class="final" href="/sdk/ruby/"><span>Ruby SDK</span></a></li><li class="level-3"><a class="final" href="/community/projects/#sdks"><span>Community Powered SDKs</span></a></li></ul></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Deploying an Engine</span></a><ul><li class="level-2"><a class="final" href="/deploy/"><span>Deploying as a Web Service</span></a></li><li class="level-2"><a class="final" href="/batchpredict/"><span>Batch Predictions</span></a></li><li class="level -2"><a class="final" href="/deploy/monitoring/"><span>Monitoring Engine</span></a></li><li class="level-2"><a class="final" href="/deploy/engineparams/"><span>Setting Engine Parameters</span></a></li><li class="level-2"><a class="final" href="/deploy/enginevariants/"><span>Deploying Multiple Engine Variants</span></a></li><li class="level-2"><a class="final" href="/deploy/plugin/"><span>Engine Server Plugin</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Customizing an Engine</span></a><ul><li class="level-2"><a class="final" href="/customize/"><span>Learning DASE</span></a></li><li class="level-2"><a class="final" href="/customize/dase/"><span>Implement DASE</span></a></li><li class="level-2"><a class="final" href="/customize/troubleshooting/"><span>Troubleshooting Engine Development</span></a></li><li class="level-2"><a class="final" href="/api/current/#package"><span>Engine Scala APIs</span></a></li></ul></li><li class="level-1"><a class="expandi ble" href="#"><span>Collecting and Analyzing Data</span></a><ul><li class="level-2"><a class="final" href="/datacollection/"><span>Event Server Overview</span></a></li><li class="level-2"><a class="final" href="/datacollection/eventapi/"><span>Collecting Data with REST/SDKs</span></a></li><li class="level-2"><a class="final" href="/datacollection/eventmodel/"><span>Events Modeling</span></a></li><li class="level-2"><a class="final" href="/datacollection/webhooks/"><span>Unifying Multichannel Data with Webhooks</span></a></li><li class="level-2"><a class="final" href="/datacollection/channel/"><span>Channel</span></a></li><li class="level-2"><a class="final" href="/datacollection/batchimport/"><span>Importing Data in Batch</span></a></li><li class="level-2"><a class="final" href="/datacollection/analytics/"><span>Using Analytics Tools</span></a></li><li class="level-2"><a class="final" href="/datacollection/plugin/"><span>Event Server Plugin</span></a></li></ul></li><li class="level- 1"><a class="expandible" href="#"><span>Choosing an Algorithm</span></a><ul><li class="level-2"><a class="final" href="/algorithm/"><span>Built-in Algorithm Libraries</span></a></li><li class="level-2"><a class="final" href="/algorithm/switch/"><span>Switching to Another Algorithm</span></a></li><li class="level-2"><a class="final" href="/algorithm/multiple/"><span>Combining Multiple Algorithms</span></a></li><li class="level-2"><a class="final" href="/algorithm/custom/"><span>Adding Your Own Algorithms</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Tuning and Evaluation</span></a><ul><li class="level-2"><a class="final" href="/evaluation/"><span>Overview</span></a></li><li class="level-2"><a class="final" href="/evaluation/paramtuning/"><span>Hyperparameter Tuning</span></a></li><li class="level-2"><a class="final" href="/evaluation/evaluationdashboard/"><span>Evaluation Dashboard</span></a></li><li class="level-2"><a class="final" href="/evaluati on/metricchoose/"><span>Choosing Evaluation Metrics</span></a></li><li class="level-2"><a class="final" href="/evaluation/metricbuild/"><span>Building Evaluation Metrics</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>System Architecture</span></a><ul><li class="level-2"><a class="final" href="/system/"><span>Architecture Overview</span></a></li><li class="level-2"><a class="final" href="/system/anotherdatastore/"><span>Using Another Data Store</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>PredictionIO® Official Templates</span></a><ul><li class="level-2"><a class="final" href="/templates/"><span>Intro</span></a></li><li class="level-2"><a class="expandible" href="#"><span>Recommendation</span></a><ul><li class="level-3"><a class="final" href="/templates/recommendation/quickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/dase/"><span>DASE</span></a></l i><li class="level-3"><a class="final" href="/templates/recommendation/evaluation/"><span>Evaluation Explained</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/reading-custom-events/"><span>Read Custom Events</span></a></li><li class="level-3"><a class="final" 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" href="/templates/recommendation/training-with-implicit-preference/"><span>Train with Implicit Preference</span></a></li><li class="level-3"><a class="final" href="/templates/recommendation/blacklist-items/"><span>Filter Recommended Items by Blacklist in Query</span></a></li><li class="level-3"><a class="final" href="/templates/ recommendation/batch-evaluator/"><span>Batch Persistable Evaluator</span></a></li></ul></li><li class="level-2"><a class="expandible" href="#"><span>E-Commerce Recommendation</span></a><ul><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/quickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/ecommercerecommendation/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" 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" href="#"><span>Similar Product</span></a><ul><li class="level-3"><a class="final" href="/templates/similarproduct/quickstart/"><span>Q uick Start</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li 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 Properties</span></a></li><li class="level-3"><a class="final" href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate Event</span></a></li><li class="level-3"><a class="final" 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="/templates/classification/quickstart/"><span>Quick Start</span></a></li><li class="level-3"><a class="final" href="/templates/classification/dase/"><span>DASE</span></a></li><li class="level-3"><a class="final" href="/templates/classification/how-to/"><span>How-To</span></a></li><li class="level-3"><a class="final" href="/templates/classification/add-algorithm/"><span>Use Alternative Algorithm</span></a></li><li class="level-3"><a class="final" href="/templates/classification/reading-custom-properties/"><span>Read Custom Properties</span></a></li></ul></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Engine Template Gallery</span></a><ul><li class="level-2"><a class="final" href="/gallery/template-gallery/"><span>Browse</span></a></li><li class="level-2"><a class="final" href="/community/submit-template/"><span>Submit your Engine as a Template</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Demo Tutorials</span></a><ul><li class="level-2"><a class="final" href="/community/projects/#demos"><span>Community Contributed Demo</span></a></li><li class="level-2"><a class="final" href="/demo/textclassification/"><span>Text Classification Engine Tutorial</span></a></li></ul></li><li class="level-1"><a class="expandible" href="/community/"><span>Getting Involved</span></a><ul><li class="level-2"><a class="final" href="/community/contribute-code/"><span>Contribute Code</span></a></li><li class="level-2"><a class="final" href="/community/contribute-documentation/"><span>Contribute Documentation</span></a></li><li class="level-2"><a class="final" href="/community/contribute-sdk/"><span>Contribute a SDK</span></a></li><li class="level-2"><a class="final" href="/community/contribute-webhook/"><span>Contribute a Webhook</span></a></li><li class="level-2"><a class="final" href="/community/projects/"><span>Community Projects</span></a></li></ul></li><l i class="level-1"><a class="expandible" href="#"><span>Getting Help</span></a><ul><li class="level-2"><a class="final" href="/resources/faq/"><span>FAQs</span></a></li><li class="level-2"><a class="final" href="/support/"><span>Support</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Resources</span></a><ul><li class="level-2"><a class="final" href="/cli/"><span>Command-line Interface</span></a></li><li class="level-2"><a class="final" href="/resources/release/"><span>Release Cadence</span></a></li><li class="level-2"><a class="final" href="/resources/intellij/"><span>Developing Engines with IntelliJ IDEA</span></a></li><li class="level-2"><a class="final" href="/resources/upgrade/"><span>Upgrade Instructions</span></a></li><li class="level-2"><a class="final" href="/resources/glossary/"><span>Glossary</span></a></li></ul></li><li class="level-1"><a class="expandible" href="#"><span>Apache Software Foundation</span></a><ul><li class="level-2"><a clas s="final" href="https://www.apache.org/";><span>Apache Homepage</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/licenses/";><span>License</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/sponsorship.html";><span>Sponsorship</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/foundation/thanks.html";><span>Thanks</span></a></li><li class="level-2"><a class="final" href="https://www.apache.org/security/";><span>Security</span></a></li></ul></li></ul></nav></div><div 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 Learnin g</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/predictionio/tree/livedoc/docs/manual/source/archived/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 we want to predict \(Y\) using \(X\), we are trying to answer the question: how doe s 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 <st rong>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";>StackOverF low</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 these transformations of our observations, they are now in standard form and can be us ed 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 class="seperator"></div><div class="row"><div class="col-md-6 footer-link-column"><div class= "footer-link-column-row"><h4>Community</h4><ul><li><a href="//predictionio.apache.org/install/" target="blank">Download</a></li><li><a href="//predictionio.apache.org/" target="blank">Docs</a></li><li><a href="//github.com/apache/predictionio" target="blank">GitHub</a></li><li><a href="mailto:[email protected]"; target="blank">Subscribe to User Mailing List</a></li><li><a href="//stackoverflow.com/questions/tagged/predictionio" target="blank">Stackoverflow</a></li></ul></div></div><div class="col-md-6 footer-link-column"><div class="footer-link-column-row"><h4>Contribute</h4><ul><li><a href="//predictionio.apache.org/community/contribute-code/" target="blank">Contribute</a></li><li><a href="//github.com/apache/predictionio" target="blank">Source Code</a></li><li><a href="//issues.apache.org/jira/browse/PIO" target="blank">Bug Tracker</a></li><li><a href="mailto:[email protected]"; target="blank">Subscribe to Development Mailing List</a></li></u l></div></div></div><div class="row"><div class="col-md-12 footer-link-column"><p>Apache PredictionIO, PredictionIO, Apache, the Apache feather logo, and the Apache PredictionIO project logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.</p><p>All other marks mentioned may be trademarks or registered trademarks of their respective owners.</p></div></div></div><div id="footer-bottom"><div class="container"><div class="row"><div class="col-md-12"><div id="footer-logo-wrapper"><img alt="PredictionIO" src="/images/logos/logo-white-d1e9c6e6.png"/><span>®</span></div><div id="social-icons-wrapper"><a class="github-button" href="https://github.com/apache/predictionio"; data-icon="octicon-star" data-show-count="true" aria-label="Star apache/predictionio on GitHub">Star</a> <a class="github-button" href="https://github.com/apache/predictionio/fork"; data-icon="octicon-repo-forked" data-show-count="true" aria-label="Fo rk apache/predictionio on GitHub">Fork</a> <script id="github-bjs" async="" defer="" src="https://buttons.github.io/buttons.js";></script><a href="https://twitter.com/predictionio"; target="blank"><img alt="PredictionIO on Twitter" src="/images/icons/twitter-ea9dc152.png"/></a> <a href="https://www.facebook.com/predictionio"; target="blank"><img alt="PredictionIO on Facebook" src="/images/icons/facebook-5c57939c.png"/></a> </div></div></div></div></div></footer></div><script>(function(w,d,t,u,n,s,e){w['SwiftypeObject']=n;w[n]=w[n]||function(){ +(w[n].q=w[n].q||[]).push(arguments);};s=d.createElement(t); +e=d.getElementsByTagName(t)[0];s.async=1;s.src=u;e.parentNode.insertBefore(s,e); +})(window,document,'script','//s.swiftypecdn.com/install/v1/st.js','_st'); + +_st('install','HaUfpXXV87xoB_zzCQ45');</script><script src="/javascripts/application-d943a254.js"></script></body></html> \ No newline at end of file
