http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/d9012d92/userguide/binaryclass/a9a_lr.html ---------------------------------------------------------------------- diff --git a/userguide/binaryclass/a9a_lr.html b/userguide/binaryclass/a9a_lr.html index 13009c6..8e2958c 100644 --- a/userguide/binaryclass/a9a_lr.html +++ b/userguide/binaryclass/a9a_lr.html @@ -100,7 +100,7 @@ <link rel="next" href="a9a_minibatch.html" /> - <link rel="prev" href="a9a_dataset.html" /> + <link rel="prev" href="a9a_generic.html" /> </head> @@ -972,7 +972,7 @@ <b>6.2.1.</b> - Data preparation + Data Preparation </a> @@ -980,13 +980,28 @@ </li> - <li class="chapter active" data-level="6.2.2" data-path="a9a_lr.html"> + <li class="chapter " data-level="6.2.2" data-path="a9a_generic.html"> - <a href="a9a_lr.html"> + <a href="a9a_generic.html"> <b>6.2.2.</b> + General Binary Classifier + + </a> + + + + </li> + + <li class="chapter active" data-level="6.2.3" data-path="a9a_lr.html"> + + <a href="a9a_lr.html"> + + + <b>6.2.3.</b> + Logistic Regression </a> @@ -995,14 +1010,14 @@ </li> - <li class="chapter " data-level="6.2.3" data-path="a9a_minibatch.html"> + <li class="chapter " data-level="6.2.4" data-path="a9a_minibatch.html"> <a href="a9a_minibatch.html"> - <b>6.2.3.</b> + <b>6.2.4.</b> - Mini-batch gradient descent + Mini-batch Gradient Descent </a> @@ -1038,7 +1053,7 @@ <b>6.3.1.</b> - Data preparation + Data Preparation </a> @@ -1076,13 +1091,28 @@ </li> - <li class="chapter " data-level="6.3.4" data-path="news20_adagrad.html"> + <li class="chapter " data-level="6.3.4" data-path="news20_generic.html"> - <a href="news20_adagrad.html"> + <a href="news20_generic.html"> <b>6.3.4.</b> + General Binary Classifier + + </a> + + + + </li> + + <li class="chapter " data-level="6.3.5" data-path="news20_adagrad.html"> + + <a href="news20_adagrad.html"> + + + <b>6.3.5.</b> + AdaGradRDA, AdaGrad, AdaDelta </a> @@ -1091,12 +1121,12 @@ </li> - <li class="chapter " data-level="6.3.5" data-path="news20_rf.html"> + <li class="chapter " data-level="6.3.6" data-path="news20_rf.html"> <a href="news20_rf.html"> - <b>6.3.5.</b> + <b>6.3.6.</b> Random Forest @@ -1134,7 +1164,7 @@ <b>6.4.1.</b> - Data preparation + Data Preparation </a> @@ -1185,7 +1215,7 @@ <b>6.5.1.</b> - Data preparation + Data Preparation </a> @@ -1236,7 +1266,7 @@ <b>6.6.1.</b> - Data pareparation + Data Pareparation </a> @@ -1302,7 +1332,7 @@ <b>6.8.1.</b> - Data preparation + Data Preparation </a> @@ -1360,7 +1390,7 @@ <b>7.1.1.</b> - Data preparation + Data Preparation </a> @@ -1375,7 +1405,7 @@ <b>7.1.2.</b> - Data preparation for one-vs-the-rest classifiers + Data Preparation for one-vs-the-rest classifiers </a> @@ -1435,7 +1465,7 @@ <b>7.1.6.</b> - one-vs-the-rest classifier + one-vs-the-rest Classifier </a> @@ -1559,7 +1589,7 @@ <b>8.2.1.</b> - Data preparation + Data Preparation </a> @@ -1567,13 +1597,28 @@ </li> - <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_arow.html"> + <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_generic.html"> - <a href="../regression/e2006_arow.html"> + <a href="../regression/e2006_generic.html"> <b>8.2.2.</b> + General Regessor + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.3" data-path="../regression/e2006_arow.html"> + + <a href="../regression/e2006_arow.html"> + + + <b>8.2.3.</b> + Passive Aggressive, AROW </a> @@ -1610,7 +1655,7 @@ <b>8.3.1.</b> - Data preparation + Data Preparation </a> @@ -1698,7 +1743,7 @@ <b>9.1.1.</b> - Item-based collaborative filtering + Item-based Collaborative Filtering </a> @@ -1734,7 +1779,7 @@ <b>9.2.1.</b> - Data preparation + Data Preparation </a> @@ -1749,7 +1794,7 @@ <b>9.2.2.</b> - LSH/MinHash and Jaccard similarity + LSH/MinHash and Jaccard Similarity </a> @@ -1764,7 +1809,7 @@ <b>9.2.3.</b> - LSH/MinHash and brute-force search + LSH/MinHash and Brute-force Search </a> @@ -1815,7 +1860,7 @@ <b>9.3.1.</b> - Data preparation + Data Preparation </a> @@ -1830,7 +1875,7 @@ <b>9.3.2.</b> - Item-based collaborative filtering + Item-based Collaborative Filtering </a> @@ -1875,7 +1920,7 @@ <b>9.3.5.</b> - SLIM for fast top-k recommendation + SLIM for fast top-k Recommendation </a> @@ -1890,7 +1935,7 @@ <b>9.3.6.</b> - 10-fold cross validation (Matrix Factorization) + 10-fold Cross Validation (Matrix Factorization) </a> @@ -2080,7 +2125,7 @@ <b>13.2.1.</b> - a9a tutorial for DataFrame + a9a Tutorial for DataFrame </a> @@ -2095,7 +2140,7 @@ <b>13.2.2.</b> - a9a tutorial for SQL + a9a Tutorial for SQL </a> @@ -2131,7 +2176,7 @@ <b>13.3.1.</b> - E2006-tfidf regression tutorial for DataFrame + E2006-tfidf Regression Tutorial for DataFrame </a> @@ -2146,7 +2191,7 @@ <b>13.3.2.</b> - E2006-tfidf regression tutorial for SQL + E2006-tfidf Regression Tutorial for SQL </a> @@ -2166,7 +2211,7 @@ <b>13.4.</b> - Generic features + Generic Features </a> @@ -2182,7 +2227,7 @@ <b>13.4.1.</b> - Top-k join processing + Top-k Join Processing </a> @@ -2197,7 +2242,7 @@ <b>13.4.2.</b> - Other utility functions + Other Utility Functions </a> @@ -2317,6 +2362,8 @@ specific language governing permissions and limitations under the License. --> +<p>This pages shows an example of applying logistic regression for a9a binary classification task.</p> +<div class="panel panel-warning"><div class="panel-heading"><h3 class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> Caution</h3></div><div class="panel-body"><p><code>logloss()</code> became deprecated since v0.5.0 release. Use smarter <a href="a9a_generic.html">general classifier</a> instead.</p></div></div> <!-- toc --><div id="toc" class="toc"> <ul> @@ -2349,7 +2396,7 @@ ) t <span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span> feature; </code></pre> -<p><em>"-total_steps" option is optional for logress() function.</em><br><em>I recommend you NOT to use options (e.g., total_steps and eta0) if you are not familiar with those options. Hivemall then uses an autonomic ETA (learning rate) estimator.</em></p> +<div class="panel panel-primary"><div class="panel-heading"><h3 class="panel-title" id="note"><i class="fa fa-edit"></i> Note</h3></div><div class="panel-body"><p><code>-total_steps</code> option is optional for logress() function. We recommend you NOT to use options (e.g., <code>total_steps</code> and <code>eta0</code>) if you are not familiar with those options. Hivemall then uses an autonomic ETA (learning rate) estimator.</p></div></div> <h1 id="prediction">prediction</h1> <pre><code class="lang-sql"><span class="hljs-keyword">create</span> <span class="hljs-keyword">or</span> <span class="hljs-keyword">replace</span> <span class="hljs-keyword">view</span> a9a_predict1 <span class="hljs-keyword">as</span> @@ -2443,7 +2490,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; gitbook.push(function() { - gitbook.page.hasChanged({"page":{"title":"Logistic Regression","level":"6.2.2","depth":2,"next":{"title":"Mini-batch gradient descent","level":"6.2.3","depth":2,"path":"binaryclass/a9a_minibatch.md","ref":"binaryclass/a9a_minibatch.md","articles":[]},"previous":{"title":"Data preparation","level":"6.2.1","depth":2,"path":"binaryclass/a9a_dataset.md","ref":"binaryclass/a9a_dataset.md","articles":[]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"h2lb":3,"header":1,"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apache/incuba tor-hivemall/"},"splitter":{},"search":{},"downloadpdf":{"base":"https://github.com/apache/incubator-hivemall/docs/gitbook","label":"PDF","multilingual":false},"multipart":{},"localized-footer":{"filename":"FOOTER.md","hline":"true"},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"katex":{},"fontsettings":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"https://hivemall.incubator.apache.org/"},"theme-api":{"languages":[],"split":false,"theme":"dark"},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"edit-link":{"label":"Edit","base":"https://github.com/apache/incubator-hivemall/tree/master/docs/gitbook"},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"}, "showLevel":true},"anchorjs":{"selector":"h1,h2,h3,*:not(.callout) > h4,h5"},"toggle-chapters":{},"expandable-chapters":{}},"theme":"default","pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"Hivemall User Manual","links":{"sidebar":{"<i class=\"fa fa-home\"></i> Home":"https://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"binaryclass/a9a_lr.md","mtime":"2018-10-18T10:26:56.664Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2018-11-13T09:32:29.643Z"},"basePath":"..","book":{"language":""}}); + gitbook.page.hasChanged({"page":{"title":"Logistic Regression","level":"6.2.3","depth":2,"next":{"title":"Mini-batch Gradient Descent","level":"6.2.4","depth":2,"path":"binaryclass/a9a_minibatch.md","ref":"binaryclass/a9a_minibatch.md","articles":[]},"previous":{"title":"General Binary Classifier","level":"6.2.2","depth":2,"path":"binaryclass/a9a_generic.md","ref":"binaryclass/a9a_generic.md","articles":[]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"h2lb":3,"header":1,"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apac he/incubator-hivemall/"},"splitter":{},"search":{},"downloadpdf":{"base":"https://github.com/apache/incubator-hivemall/docs/gitbook","label":"PDF","multilingual":false},"multipart":{},"localized-footer":{"filename":"FOOTER.md","hline":"true"},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"katex":{},"fontsettings":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"https://hivemall.incubator.apache.org/"},"theme-api":{"languages":[],"split":false,"theme":"dark"},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"edit-link":{"label":"Edit","base":"https://github.com/apache/incubator-hivemall/tree/master/docs/gitbook"},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/pri nt.css"},"showLevel":true},"anchorjs":{"selector":"h1,h2,h3,*:not(.callout) > h4,h5"},"toggle-chapters":{},"expandable-chapters":{}},"theme":"default","pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"Hivemall User Manual","links":{"sidebar":{"<i class=\"fa fa-home\"></i> Home":"https://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"binaryclass/a9a_lr.md","mtime":"2018-12-26T10:16:03.077Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2018-12-26T10:20:07.153Z"},"basePath":"..","book":{"language":""}}); }); </script> </div> @@ -2473,7 +2520,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda - <script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/4.1.1/anchor.min.js"></script> + <script src="../gitbook/gitbook-plugin-anchorjs/anchor.min.js"></script>
http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/d9012d92/userguide/binaryclass/a9a_minibatch.html ---------------------------------------------------------------------- diff --git a/userguide/binaryclass/a9a_minibatch.html b/userguide/binaryclass/a9a_minibatch.html index 7554705..7e33ddc 100644 --- a/userguide/binaryclass/a9a_minibatch.html +++ b/userguide/binaryclass/a9a_minibatch.html @@ -4,7 +4,7 @@ <head> <meta charset="UTF-8"> <meta content="text/html; charset=utf-8" http-equiv="Content-Type"> - <title>Mini-batch gradient descent · Hivemall User Manual</title> + <title>Mini-batch Gradient Descent · Hivemall User Manual</title> <meta http-equiv="X-UA-Compatible" content="IE=edge" /> <meta name="description" content=""> <meta name="generator" content="GitBook 3.2.3"> @@ -972,7 +972,7 @@ <b>6.2.1.</b> - Data preparation + Data Preparation </a> @@ -980,13 +980,28 @@ </li> - <li class="chapter " data-level="6.2.2" data-path="a9a_lr.html"> + <li class="chapter " data-level="6.2.2" data-path="a9a_generic.html"> - <a href="a9a_lr.html"> + <a href="a9a_generic.html"> <b>6.2.2.</b> + General Binary Classifier + + </a> + + + + </li> + + <li class="chapter " data-level="6.2.3" data-path="a9a_lr.html"> + + <a href="a9a_lr.html"> + + + <b>6.2.3.</b> + Logistic Regression </a> @@ -995,14 +1010,14 @@ </li> - <li class="chapter active" data-level="6.2.3" data-path="a9a_minibatch.html"> + <li class="chapter active" data-level="6.2.4" data-path="a9a_minibatch.html"> <a href="a9a_minibatch.html"> - <b>6.2.3.</b> + <b>6.2.4.</b> - Mini-batch gradient descent + Mini-batch Gradient Descent </a> @@ -1038,7 +1053,7 @@ <b>6.3.1.</b> - Data preparation + Data Preparation </a> @@ -1076,13 +1091,28 @@ </li> - <li class="chapter " data-level="6.3.4" data-path="news20_adagrad.html"> + <li class="chapter " data-level="6.3.4" data-path="news20_generic.html"> - <a href="news20_adagrad.html"> + <a href="news20_generic.html"> <b>6.3.4.</b> + General Binary Classifier + + </a> + + + + </li> + + <li class="chapter " data-level="6.3.5" data-path="news20_adagrad.html"> + + <a href="news20_adagrad.html"> + + + <b>6.3.5.</b> + AdaGradRDA, AdaGrad, AdaDelta </a> @@ -1091,12 +1121,12 @@ </li> - <li class="chapter " data-level="6.3.5" data-path="news20_rf.html"> + <li class="chapter " data-level="6.3.6" data-path="news20_rf.html"> <a href="news20_rf.html"> - <b>6.3.5.</b> + <b>6.3.6.</b> Random Forest @@ -1134,7 +1164,7 @@ <b>6.4.1.</b> - Data preparation + Data Preparation </a> @@ -1185,7 +1215,7 @@ <b>6.5.1.</b> - Data preparation + Data Preparation </a> @@ -1236,7 +1266,7 @@ <b>6.6.1.</b> - Data pareparation + Data Pareparation </a> @@ -1302,7 +1332,7 @@ <b>6.8.1.</b> - Data preparation + Data Preparation </a> @@ -1360,7 +1390,7 @@ <b>7.1.1.</b> - Data preparation + Data Preparation </a> @@ -1375,7 +1405,7 @@ <b>7.1.2.</b> - Data preparation for one-vs-the-rest classifiers + Data Preparation for one-vs-the-rest classifiers </a> @@ -1435,7 +1465,7 @@ <b>7.1.6.</b> - one-vs-the-rest classifier + one-vs-the-rest Classifier </a> @@ -1559,7 +1589,7 @@ <b>8.2.1.</b> - Data preparation + Data Preparation </a> @@ -1567,13 +1597,28 @@ </li> - <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_arow.html"> + <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_generic.html"> - <a href="../regression/e2006_arow.html"> + <a href="../regression/e2006_generic.html"> <b>8.2.2.</b> + General Regessor + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.3" data-path="../regression/e2006_arow.html"> + + <a href="../regression/e2006_arow.html"> + + + <b>8.2.3.</b> + Passive Aggressive, AROW </a> @@ -1610,7 +1655,7 @@ <b>8.3.1.</b> - Data preparation + Data Preparation </a> @@ -1698,7 +1743,7 @@ <b>9.1.1.</b> - Item-based collaborative filtering + Item-based Collaborative Filtering </a> @@ -1734,7 +1779,7 @@ <b>9.2.1.</b> - Data preparation + Data Preparation </a> @@ -1749,7 +1794,7 @@ <b>9.2.2.</b> - LSH/MinHash and Jaccard similarity + LSH/MinHash and Jaccard Similarity </a> @@ -1764,7 +1809,7 @@ <b>9.2.3.</b> - LSH/MinHash and brute-force search + LSH/MinHash and Brute-force Search </a> @@ -1815,7 +1860,7 @@ <b>9.3.1.</b> - Data preparation + Data Preparation </a> @@ -1830,7 +1875,7 @@ <b>9.3.2.</b> - Item-based collaborative filtering + Item-based Collaborative Filtering </a> @@ -1875,7 +1920,7 @@ <b>9.3.5.</b> - SLIM for fast top-k recommendation + SLIM for fast top-k Recommendation </a> @@ -1890,7 +1935,7 @@ <b>9.3.6.</b> - 10-fold cross validation (Matrix Factorization) + 10-fold Cross Validation (Matrix Factorization) </a> @@ -2080,7 +2125,7 @@ <b>13.2.1.</b> - a9a tutorial for DataFrame + a9a Tutorial for DataFrame </a> @@ -2095,7 +2140,7 @@ <b>13.2.2.</b> - a9a tutorial for SQL + a9a Tutorial for SQL </a> @@ -2131,7 +2176,7 @@ <b>13.3.1.</b> - E2006-tfidf regression tutorial for DataFrame + E2006-tfidf Regression Tutorial for DataFrame </a> @@ -2146,7 +2191,7 @@ <b>13.3.2.</b> - E2006-tfidf regression tutorial for SQL + E2006-tfidf Regression Tutorial for SQL </a> @@ -2166,7 +2211,7 @@ <b>13.4.</b> - Generic features + Generic Features </a> @@ -2182,7 +2227,7 @@ <b>13.4.1.</b> - Top-k join processing + Top-k Join Processing </a> @@ -2197,7 +2242,7 @@ <b>13.4.2.</b> - Other utility functions + Other Utility Functions </a> @@ -2284,7 +2329,7 @@ <!-- Title --> <h1> <i class="fa fa-circle-o-notch fa-spin"></i> - <a href=".." >Mini-batch gradient descent</a> + <a href=".." >Mini-batch Gradient Descent</a> </h1> </div> @@ -2317,8 +2362,16 @@ specific language governing permissions and limitations under the License. --> -<p>This page explains how to apply <a href="https://class.coursera.org/ml-003/lecture/106" target="_blank">Mini-Batch Gradient Descent</a> for the training of logistic regression explained in <a href="a9a_lr.html">this example</a>. -So, refer <a href="a9a_lr.html">this page</a> first. This content depends on it.</p> +<p>This page explains how to apply <a href="https://class.coursera.org/ml-003/lecture/106" target="_blank">Mini-Batch Gradient Descent</a> for the training of logistic regression explained in <a href="a9a_lr.html">this example</a>. So, refer <a href="a9a_lr.html">this page</a> first. This content depends on it.</p> +<div class="panel panel-warning"><div class="panel-heading"><h3 class="panel-title" id="caution"><i class="fa fa-exclamation-triangle"></i> Caution</h3></div><div class="panel-body"><p><code>logloss()</code> became deprecated since v0.5.0 release. Use smarter <a href="a9a_generic.html">general classifier</a> instead. You can use <code>-mini_batch</code> option in general classifier as well.</p></div></div> +<!-- toc --><div id="toc" class="toc"> + +<ul> +<li><a href="#training">Training</a></li> +<li><a href="#evaluation">Evaluation</a></li> +</ul> + +</div><!-- tocstop --> <h1 id="training">Training</h1> <p>Replace <code>a9a_model1</code> of <a href="a9a_lr.html">this example</a>.</p> <pre><code class="lang-sql"><span class="hljs-keyword">set</span> hivevar:total_steps=<span class="hljs-number">32561</span>; @@ -2410,7 +2463,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; gitbook.push(function() { - gitbook.page.hasChanged({"page":{"title":"Mini-batch gradient descent","level":"6.2.3","depth":2,"next":{"title":"News20 Tutorial","level":"6.3","depth":1,"path":"binaryclass/news20.md","ref":"binaryclass/news20.md","articles":[{"title":"Data preparation","level":"6.3.1","depth":2,"path":"binaryclass/news20_dataset.md","ref":"binaryclass/news20_dataset.md","articles":[]},{"title":"Perceptron, Passive Aggressive","level":"6.3.2","depth":2,"path":"binaryclass/news20_pa.md","ref":"binaryclass/news20_pa.md","articles":[]},{"title":"CW, AROW, SCW","level":"6.3.3","depth":2,"path":"binaryclass/news20_scw.md","ref":"binaryclass/news20_scw.md","articles":[]},{"title":"AdaGradRDA, AdaGrad, AdaDelta","level":"6.3.4","depth":2,"path":"binaryclass/news20_adagrad.md","ref":"binaryclass/news20_adagrad.md","articles":[]},{"title":"Random Forest","level":"6.3.5","depth":2,"path":"binaryclass/news20_rf.md","ref":"binaryclass/news20_rf.md","articles":[]}]},"previous":{"title":"Logistic Re gression","level":"6.2.2","depth":2,"path":"binaryclass/a9a_lr.md","ref":"binaryclass/a9a_lr.md","articles":[]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"h2lb":3,"header":1,"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apache/incubator-hivemall/"},"splitter":{},"search":{},"downloadpdf":{"base":"https://github.com/apache/incubator-hivemall/docs/gitbook","label":"PDF","multilingual":false},"multipart":{},"localized-footer":{"filename":"FOOTER.md","hline":"true"},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"kat ex":{},"fontsettings":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"https://hivemall.incubator.apache.org/"},"theme-api":{"languages":[],"split":false,"theme":"dark"},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"edit-link":{"label":"Edit","base":"https://github.com/apache/incubator-hivemall/tree/master/docs/gitbook"},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"showLevel":true},"anchorjs":{"selector":"h1,h2,h3,*:not(.callout) > h4,h5"},"toggle-chapters":{},"expandable-chapters":{}},"theme":"default","pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"t op":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"Hivemall User Manual","links":{"sidebar":{"<i class=\"fa fa-home\"></i> Home":"https://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"binaryclass/a9a_minibatch.md","mtime":"2018-10-18T10:26:56.665Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2018-11-13T09:32:29.643Z"},"basePath":"..","book":{"language":""}}); + gitbook.page.hasChanged({"page":{"title":"Mini-batch Gradient Descent","level":"6.2.4","depth":2,"next":{"title":"News20 Tutorial","level":"6.3","depth":1,"path":"binaryclass/news20.md","ref":"binaryclass/news20.md","articles":[{"title":"Data Preparation","level":"6.3.1","depth":2,"path":"binaryclass/news20_dataset.md","ref":"binaryclass/news20_dataset.md","articles":[]},{"title":"Perceptron, Passive Aggressive","level":"6.3.2","depth":2,"path":"binaryclass/news20_pa.md","ref":"binaryclass/news20_pa.md","articles":[]},{"title":"CW, AROW, SCW","level":"6.3.3","depth":2,"path":"binaryclass/news20_scw.md","ref":"binaryclass/news20_scw.md","articles":[]},{"title":"General Binary Classifier","level":"6.3.4","depth":2,"path":"binaryclass/news20_generic.md","ref":"binaryclass/news20_generic.md","articles":[]},{"title":"AdaGradRDA, AdaGrad, AdaDelta","level":"6.3.5","depth":2,"path":"binaryclass/news20_adagrad.md","ref":"binaryclass/news20_adagrad.md","articles":[]},{"title":"Ra ndom Forest","level":"6.3.6","depth":2,"path":"binaryclass/news20_rf.md","ref":"binaryclass/news20_rf.md","articles":[]}]},"previous":{"title":"Logistic Regression","level":"6.2.3","depth":2,"path":"binaryclass/a9a_lr.md","ref":"binaryclass/a9a_lr.md","articles":[]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"h2lb":3,"header":1,"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apache/incubator-hivemall/"},"splitter":{},"search":{},"downloadpdf":{"base":"https://github.com/apache/incubator-hivemall/docs/gitbook","label":"PDF","multilin gual":false},"multipart":{},"localized-footer":{"filename":"FOOTER.md","hline":"true"},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"katex":{},"fontsettings":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"https://hivemall.incubator.apache.org/"},"theme-api":{"languages":[],"split":false,"theme":"dark"},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"edit-link":{"label":"Edit","base":"https://github.com/apache/incubator-hivemall/tree/master/docs/gitbook"},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"showLevel":true},"anchorjs":{"selector":"h1,h2,h3,*:not(.callout) > h4,h5"},"toggle-chapters":{},"expandable-chapters":{}},"theme":"default","pdf" :{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"Hivemall User Manual","links":{"sidebar":{"<i class=\"fa fa-home\"></i> Home":"https://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"binaryclass/a9a_minibatch.md","mtime":"2018-12-26T10:16:03.078Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2018-12-26T10:20:07.153Z"},"basePath":"..","book":{"language":""}}); }); </script> </div> @@ -2440,7 +2493,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda - <script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/4.1.1/anchor.min.js"></script> + <script src="../gitbook/gitbook-plugin-anchorjs/anchor.min.js"></script> http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/d9012d92/userguide/binaryclass/criteo.html ---------------------------------------------------------------------- diff --git a/userguide/binaryclass/criteo.html b/userguide/binaryclass/criteo.html index b022418..30a8576 100644 --- a/userguide/binaryclass/criteo.html +++ b/userguide/binaryclass/criteo.html @@ -972,7 +972,7 @@ <b>6.2.1.</b> - Data preparation + Data Preparation </a> @@ -980,13 +980,28 @@ </li> - <li class="chapter " data-level="6.2.2" data-path="a9a_lr.html"> + <li class="chapter " data-level="6.2.2" data-path="a9a_generic.html"> - <a href="a9a_lr.html"> + <a href="a9a_generic.html"> <b>6.2.2.</b> + General Binary Classifier + + </a> + + + + </li> + + <li class="chapter " data-level="6.2.3" data-path="a9a_lr.html"> + + <a href="a9a_lr.html"> + + + <b>6.2.3.</b> + Logistic Regression </a> @@ -995,14 +1010,14 @@ </li> - <li class="chapter " data-level="6.2.3" data-path="a9a_minibatch.html"> + <li class="chapter " data-level="6.2.4" data-path="a9a_minibatch.html"> <a href="a9a_minibatch.html"> - <b>6.2.3.</b> + <b>6.2.4.</b> - Mini-batch gradient descent + Mini-batch Gradient Descent </a> @@ -1038,7 +1053,7 @@ <b>6.3.1.</b> - Data preparation + Data Preparation </a> @@ -1076,13 +1091,28 @@ </li> - <li class="chapter " data-level="6.3.4" data-path="news20_adagrad.html"> + <li class="chapter " data-level="6.3.4" data-path="news20_generic.html"> - <a href="news20_adagrad.html"> + <a href="news20_generic.html"> <b>6.3.4.</b> + General Binary Classifier + + </a> + + + + </li> + + <li class="chapter " data-level="6.3.5" data-path="news20_adagrad.html"> + + <a href="news20_adagrad.html"> + + + <b>6.3.5.</b> + AdaGradRDA, AdaGrad, AdaDelta </a> @@ -1091,12 +1121,12 @@ </li> - <li class="chapter " data-level="6.3.5" data-path="news20_rf.html"> + <li class="chapter " data-level="6.3.6" data-path="news20_rf.html"> <a href="news20_rf.html"> - <b>6.3.5.</b> + <b>6.3.6.</b> Random Forest @@ -1134,7 +1164,7 @@ <b>6.4.1.</b> - Data preparation + Data Preparation </a> @@ -1185,7 +1215,7 @@ <b>6.5.1.</b> - Data preparation + Data Preparation </a> @@ -1236,7 +1266,7 @@ <b>6.6.1.</b> - Data pareparation + Data Pareparation </a> @@ -1302,7 +1332,7 @@ <b>6.8.1.</b> - Data preparation + Data Preparation </a> @@ -1360,7 +1390,7 @@ <b>7.1.1.</b> - Data preparation + Data Preparation </a> @@ -1375,7 +1405,7 @@ <b>7.1.2.</b> - Data preparation for one-vs-the-rest classifiers + Data Preparation for one-vs-the-rest classifiers </a> @@ -1435,7 +1465,7 @@ <b>7.1.6.</b> - one-vs-the-rest classifier + one-vs-the-rest Classifier </a> @@ -1559,7 +1589,7 @@ <b>8.2.1.</b> - Data preparation + Data Preparation </a> @@ -1567,13 +1597,28 @@ </li> - <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_arow.html"> + <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_generic.html"> - <a href="../regression/e2006_arow.html"> + <a href="../regression/e2006_generic.html"> <b>8.2.2.</b> + General Regessor + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.3" data-path="../regression/e2006_arow.html"> + + <a href="../regression/e2006_arow.html"> + + + <b>8.2.3.</b> + Passive Aggressive, AROW </a> @@ -1610,7 +1655,7 @@ <b>8.3.1.</b> - Data preparation + Data Preparation </a> @@ -1698,7 +1743,7 @@ <b>9.1.1.</b> - Item-based collaborative filtering + Item-based Collaborative Filtering </a> @@ -1734,7 +1779,7 @@ <b>9.2.1.</b> - Data preparation + Data Preparation </a> @@ -1749,7 +1794,7 @@ <b>9.2.2.</b> - LSH/MinHash and Jaccard similarity + LSH/MinHash and Jaccard Similarity </a> @@ -1764,7 +1809,7 @@ <b>9.2.3.</b> - LSH/MinHash and brute-force search + LSH/MinHash and Brute-force Search </a> @@ -1815,7 +1860,7 @@ <b>9.3.1.</b> - Data preparation + Data Preparation </a> @@ -1830,7 +1875,7 @@ <b>9.3.2.</b> - Item-based collaborative filtering + Item-based Collaborative Filtering </a> @@ -1875,7 +1920,7 @@ <b>9.3.5.</b> - SLIM for fast top-k recommendation + SLIM for fast top-k Recommendation </a> @@ -1890,7 +1935,7 @@ <b>9.3.6.</b> - 10-fold cross validation (Matrix Factorization) + 10-fold Cross Validation (Matrix Factorization) </a> @@ -2080,7 +2125,7 @@ <b>13.2.1.</b> - a9a tutorial for DataFrame + a9a Tutorial for DataFrame </a> @@ -2095,7 +2140,7 @@ <b>13.2.2.</b> - a9a tutorial for SQL + a9a Tutorial for SQL </a> @@ -2131,7 +2176,7 @@ <b>13.3.1.</b> - E2006-tfidf regression tutorial for DataFrame + E2006-tfidf Regression Tutorial for DataFrame </a> @@ -2146,7 +2191,7 @@ <b>13.3.2.</b> - E2006-tfidf regression tutorial for SQL + E2006-tfidf Regression Tutorial for SQL </a> @@ -2166,7 +2211,7 @@ <b>13.4.</b> - Generic features + Generic Features </a> @@ -2182,7 +2227,7 @@ <b>13.4.1.</b> - Top-k join processing + Top-k Join Processing </a> @@ -2197,7 +2242,7 @@ <b>13.4.2.</b> - Other utility functions + Other Utility Functions </a> @@ -2373,7 +2418,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; gitbook.push(function() { - gitbook.page.hasChanged({"page":{"title":"Criteo Tutorial","level":"6.8","depth":1,"next":{"title":"Data preparation","level":"6.8.1","depth":2,"path":"binaryclass/criteo_dataset.md","ref":"binaryclass/criteo_dataset.md","articles":[]},"previous":{"title":"Kaggle Titanic Tutorial","level":"6.7","depth":1,"path":"binaryclass/titanic_rf.md","ref":"binaryclass/titanic_rf.md","articles":[]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"h2lb":3,"header":1,"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apache/incubator-hivemall /"},"splitter":{},"search":{},"downloadpdf":{"base":"https://github.com/apache/incubator-hivemall/docs/gitbook","label":"PDF","multilingual":false},"multipart":{},"localized-footer":{"filename":"FOOTER.md","hline":"true"},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"katex":{},"fontsettings":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"https://hivemall.incubator.apache.org/"},"theme-api":{"languages":[],"split":false,"theme":"dark"},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"edit-link":{"label":"Edit","base":"https://github.com/apache/incubator-hivemall/tree/master/docs/gitbook"},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"showLevel": true},"anchorjs":{"selector":"h1,h2,h3,*:not(.callout) > h4,h5"},"toggle-chapters":{},"expandable-chapters":{}},"theme":"default","pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"Hivemall User Manual","links":{"sidebar":{"<i class=\"fa fa-home\"></i> Home":"https://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"binaryclass/criteo.md","mtime":"2018-10-18T10:26:56.666Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2018-11-13T09:32:29.643Z"},"basePath":"..","book":{"language":""}}); + gitbook.page.hasChanged({"page":{"title":"Criteo Tutorial","level":"6.8","depth":1,"next":{"title":"Data Preparation","level":"6.8.1","depth":2,"path":"binaryclass/criteo_dataset.md","ref":"binaryclass/criteo_dataset.md","articles":[]},"previous":{"title":"Kaggle Titanic Tutorial","level":"6.7","depth":1,"path":"binaryclass/titanic_rf.md","ref":"binaryclass/titanic_rf.md","articles":[]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"h2lb":3,"header":1,"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apache/incubator-hivemall /"},"splitter":{},"search":{},"downloadpdf":{"base":"https://github.com/apache/incubator-hivemall/docs/gitbook","label":"PDF","multilingual":false},"multipart":{},"localized-footer":{"filename":"FOOTER.md","hline":"true"},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"katex":{},"fontsettings":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"https://hivemall.incubator.apache.org/"},"theme-api":{"languages":[],"split":false,"theme":"dark"},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"edit-link":{"label":"Edit","base":"https://github.com/apache/incubator-hivemall/tree/master/docs/gitbook"},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"showLevel": true},"anchorjs":{"selector":"h1,h2,h3,*:not(.callout) > h4,h5"},"toggle-chapters":{},"expandable-chapters":{}},"theme":"default","pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"Hivemall User Manual","links":{"sidebar":{"<i class=\"fa fa-home\"></i> Home":"https://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"binaryclass/criteo.md","mtime":"2018-10-18T10:26:56.666Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2018-12-26T10:20:07.153Z"},"basePath":"..","book":{"language":""}}); }); </script> </div> @@ -2403,7 +2448,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda - <script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/4.1.1/anchor.min.js"></script> + <script src="../gitbook/gitbook-plugin-anchorjs/anchor.min.js"></script> http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/d9012d92/userguide/binaryclass/criteo_dataset.html ---------------------------------------------------------------------- diff --git a/userguide/binaryclass/criteo_dataset.html b/userguide/binaryclass/criteo_dataset.html index 330bc79..d28879d 100644 --- a/userguide/binaryclass/criteo_dataset.html +++ b/userguide/binaryclass/criteo_dataset.html @@ -4,7 +4,7 @@ <head> <meta charset="UTF-8"> <meta content="text/html; charset=utf-8" http-equiv="Content-Type"> - <title>Data preparation · Hivemall User Manual</title> + <title>Data Preparation · Hivemall User Manual</title> <meta http-equiv="X-UA-Compatible" content="IE=edge" /> <meta name="description" content=""> <meta name="generator" content="GitBook 3.2.3"> @@ -972,7 +972,7 @@ <b>6.2.1.</b> - Data preparation + Data Preparation </a> @@ -980,13 +980,28 @@ </li> - <li class="chapter " data-level="6.2.2" data-path="a9a_lr.html"> + <li class="chapter " data-level="6.2.2" data-path="a9a_generic.html"> - <a href="a9a_lr.html"> + <a href="a9a_generic.html"> <b>6.2.2.</b> + General Binary Classifier + + </a> + + + + </li> + + <li class="chapter " data-level="6.2.3" data-path="a9a_lr.html"> + + <a href="a9a_lr.html"> + + + <b>6.2.3.</b> + Logistic Regression </a> @@ -995,14 +1010,14 @@ </li> - <li class="chapter " data-level="6.2.3" data-path="a9a_minibatch.html"> + <li class="chapter " data-level="6.2.4" data-path="a9a_minibatch.html"> <a href="a9a_minibatch.html"> - <b>6.2.3.</b> + <b>6.2.4.</b> - Mini-batch gradient descent + Mini-batch Gradient Descent </a> @@ -1038,7 +1053,7 @@ <b>6.3.1.</b> - Data preparation + Data Preparation </a> @@ -1076,13 +1091,28 @@ </li> - <li class="chapter " data-level="6.3.4" data-path="news20_adagrad.html"> + <li class="chapter " data-level="6.3.4" data-path="news20_generic.html"> - <a href="news20_adagrad.html"> + <a href="news20_generic.html"> <b>6.3.4.</b> + General Binary Classifier + + </a> + + + + </li> + + <li class="chapter " data-level="6.3.5" data-path="news20_adagrad.html"> + + <a href="news20_adagrad.html"> + + + <b>6.3.5.</b> + AdaGradRDA, AdaGrad, AdaDelta </a> @@ -1091,12 +1121,12 @@ </li> - <li class="chapter " data-level="6.3.5" data-path="news20_rf.html"> + <li class="chapter " data-level="6.3.6" data-path="news20_rf.html"> <a href="news20_rf.html"> - <b>6.3.5.</b> + <b>6.3.6.</b> Random Forest @@ -1134,7 +1164,7 @@ <b>6.4.1.</b> - Data preparation + Data Preparation </a> @@ -1185,7 +1215,7 @@ <b>6.5.1.</b> - Data preparation + Data Preparation </a> @@ -1236,7 +1266,7 @@ <b>6.6.1.</b> - Data pareparation + Data Pareparation </a> @@ -1302,7 +1332,7 @@ <b>6.8.1.</b> - Data preparation + Data Preparation </a> @@ -1360,7 +1390,7 @@ <b>7.1.1.</b> - Data preparation + Data Preparation </a> @@ -1375,7 +1405,7 @@ <b>7.1.2.</b> - Data preparation for one-vs-the-rest classifiers + Data Preparation for one-vs-the-rest classifiers </a> @@ -1435,7 +1465,7 @@ <b>7.1.6.</b> - one-vs-the-rest classifier + one-vs-the-rest Classifier </a> @@ -1559,7 +1589,7 @@ <b>8.2.1.</b> - Data preparation + Data Preparation </a> @@ -1567,13 +1597,28 @@ </li> - <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_arow.html"> + <li class="chapter " data-level="8.2.2" data-path="../regression/e2006_generic.html"> - <a href="../regression/e2006_arow.html"> + <a href="../regression/e2006_generic.html"> <b>8.2.2.</b> + General Regessor + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.3" data-path="../regression/e2006_arow.html"> + + <a href="../regression/e2006_arow.html"> + + + <b>8.2.3.</b> + Passive Aggressive, AROW </a> @@ -1610,7 +1655,7 @@ <b>8.3.1.</b> - Data preparation + Data Preparation </a> @@ -1698,7 +1743,7 @@ <b>9.1.1.</b> - Item-based collaborative filtering + Item-based Collaborative Filtering </a> @@ -1734,7 +1779,7 @@ <b>9.2.1.</b> - Data preparation + Data Preparation </a> @@ -1749,7 +1794,7 @@ <b>9.2.2.</b> - LSH/MinHash and Jaccard similarity + LSH/MinHash and Jaccard Similarity </a> @@ -1764,7 +1809,7 @@ <b>9.2.3.</b> - LSH/MinHash and brute-force search + LSH/MinHash and Brute-force Search </a> @@ -1815,7 +1860,7 @@ <b>9.3.1.</b> - Data preparation + Data Preparation </a> @@ -1830,7 +1875,7 @@ <b>9.3.2.</b> - Item-based collaborative filtering + Item-based Collaborative Filtering </a> @@ -1875,7 +1920,7 @@ <b>9.3.5.</b> - SLIM for fast top-k recommendation + SLIM for fast top-k Recommendation </a> @@ -1890,7 +1935,7 @@ <b>9.3.6.</b> - 10-fold cross validation (Matrix Factorization) + 10-fold Cross Validation (Matrix Factorization) </a> @@ -2080,7 +2125,7 @@ <b>13.2.1.</b> - a9a tutorial for DataFrame + a9a Tutorial for DataFrame </a> @@ -2095,7 +2140,7 @@ <b>13.2.2.</b> - a9a tutorial for SQL + a9a Tutorial for SQL </a> @@ -2131,7 +2176,7 @@ <b>13.3.1.</b> - E2006-tfidf regression tutorial for DataFrame + E2006-tfidf Regression Tutorial for DataFrame </a> @@ -2146,7 +2191,7 @@ <b>13.3.2.</b> - E2006-tfidf regression tutorial for SQL + E2006-tfidf Regression Tutorial for SQL </a> @@ -2166,7 +2211,7 @@ <b>13.4.</b> - Generic features + Generic Features </a> @@ -2182,7 +2227,7 @@ <b>13.4.1.</b> - Top-k join processing + Top-k Join Processing </a> @@ -2197,7 +2242,7 @@ <b>13.4.2.</b> - Other utility functions + Other Utility Functions </a> @@ -2284,7 +2329,7 @@ <!-- Title --> <h1> <i class="fa fa-circle-o-notch fa-spin"></i> - <a href=".." >Data preparation</a> + <a href=".." >Data Preparation</a> </h1> </div> @@ -2435,7 +2480,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; gitbook.push(function() { - gitbook.page.hasChanged({"page":{"title":"Data preparation","level":"6.8.1","depth":2,"next":{"title":"Field-Aware Factorization Machines","level":"6.8.2","depth":2,"path":"binaryclass/criteo_ffm.md","ref":"binaryclass/criteo_ffm.md","articles":[]},"previous":{"title":"Criteo Tutorial","level":"6.8","depth":1,"path":"binaryclass/criteo.md","ref":"binaryclass/criteo.md","articles":[{"title":"Data preparation","level":"6.8.1","depth":2,"path":"binaryclass/criteo_dataset.md","ref":"binaryclass/criteo_dataset.md","articles":[]},{"title":"Field-Aware Factorization Machines","level":"6.8.2","depth":2,"path":"binaryclass/criteo_ffm.md","ref":"binaryclass/criteo_ffm.md","articles":[]}]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf" :"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"h2lb":3,"header":1,"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apache/incubator-hivemall/"},"splitter":{},"search":{},"downloadpdf":{"base":"https://github.com/apache/incubator-hivemall/docs/gitbook","label":"PDF","multilingual":false},"multipart":{},"localized-footer":{"filename":"FOOTER.md","hline":"true"},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"katex":{},"fontsettings":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"https://hivemall.incubator.apache.org/"},"theme-api":{"languages":[],"split":false,"theme":"dark"},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"edit-li nk":{"label":"Edit","base":"https://github.com/apache/incubator-hivemall/tree/master/docs/gitbook"},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"showLevel":true},"anchorjs":{"selector":"h1,h2,h3,*:not(.callout) > h4,h5"},"toggle-chapters":{},"expandable-chapters":{}},"theme":"default","pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"Hivemall User Manual","links":{"sidebar":{"<i class=\"fa fa-home\"></i> Home":"https://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"binaryclass/criteo_dataset.md","mtime":"2018-10-18T10:26:5 6.666Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2018-11-13T09:32:29.643Z"},"basePath":"..","book":{"language":""}}); + gitbook.page.hasChanged({"page":{"title":"Data Preparation","level":"6.8.1","depth":2,"next":{"title":"Field-Aware Factorization Machines","level":"6.8.2","depth":2,"path":"binaryclass/criteo_ffm.md","ref":"binaryclass/criteo_ffm.md","articles":[]},"previous":{"title":"Criteo Tutorial","level":"6.8","depth":1,"path":"binaryclass/criteo.md","ref":"binaryclass/criteo.md","articles":[{"title":"Data Preparation","level":"6.8.1","depth":2,"path":"binaryclass/criteo_dataset.md","ref":"binaryclass/criteo_dataset.md","articles":[]},{"title":"Field-Aware Factorization Machines","level":"6.8.2","depth":2,"path":"binaryclass/criteo_ffm.md","ref":"binaryclass/criteo_ffm.md","articles":[]}]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf" :"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"h2lb":3,"header":1,"maxdepth":3,"mindepth":1,"notoc":true},"github":{"url":"https://github.com/apache/incubator-hivemall/"},"splitter":{},"search":{},"downloadpdf":{"base":"https://github.com/apache/incubator-hivemall/docs/gitbook","label":"PDF","multilingual":false},"multipart":{},"localized-footer":{"filename":"FOOTER.md","hline":"true"},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"katex":{},"fontsettings":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"https://hivemall.incubator.apache.org/"},"theme-api":{"languages":[],"split":false,"theme":"dark"},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"edit-li nk":{"label":"Edit","base":"https://github.com/apache/incubator-hivemall/tree/master/docs/gitbook"},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"showLevel":true},"anchorjs":{"selector":"h1,h2,h3,*:not(.callout) > h4,h5"},"toggle-chapters":{},"expandable-chapters":{}},"theme":"default","pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"Hivemall User Manual","links":{"sidebar":{"<i class=\"fa fa-home\"></i> Home":"https://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"binaryclass/criteo_dataset.md","mtime":"2018-10-18T10:26:5 6.666Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2018-12-26T10:20:07.153Z"},"basePath":"..","book":{"language":""}}); }); </script> </div> @@ -2465,7 +2510,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda - <script src="https://cdnjs.cloudflare.com/ajax/libs/anchor-js/4.1.1/anchor.min.js"></script> + <script src="../gitbook/gitbook-plugin-anchorjs/anchor.min.js"></script>
