<?xml version="1.0" encoding="utf-8"?>
<?xml-stylesheet type="text/xsl" href="../assets/xml/rss.xsl" media="all"?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Joshua Hernandez's Blog (Posts about Quick Post)</title><link>http://joshuahernandezblog.com/</link><description></description><atom:link href="http://joshuahernandezblog.com/categories/quick-post.xml" rel="self" type="application/rss+xml"></atom:link><language>en</language><copyright>Contents © 2019 &lt;a href="mailto:Joshua.M.S.Hernandez@gmail.com"&gt;Joshua Hernandez&lt;/a&gt; </copyright><lastBuildDate>Wed, 16 Jan 2019 22:40:47 GMT</lastBuildDate><generator>Nikola (getnikola.com)</generator><docs>http://blogs.law.harvard.edu/tech/rss</docs><item><title>A Perfectly Cromulent Intro to Simpson's Paradox</title><link>http://joshuahernandezblog.com/blog/a-perfectly-cromulent-intro-to-simpsons-paradox/</link><dc:creator>Joshua Hernandez</dc:creator><description>&lt;div tabindex="-1" id="notebook" class="border-box-sizing"&gt;
    &lt;div class="container" id="notebook-container"&gt;

&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;
&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;&lt;i&gt;In this post, we will go over one of my favorite statistical phenomenons, Simpson's paradox, using interactive data modules.  &lt;/i&gt;
&lt;/p&gt;&lt;p&gt;&lt;a href="http://joshuahernandezblog.com/blog/a-perfectly-cromulent-intro-to-simpsons-paradox/"&gt;Read more…&lt;/a&gt; (9 min remaining to read)&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</description><category>Quick Post</category><category>Statistics</category><guid>http://joshuahernandezblog.com/blog/a-perfectly-cromulent-intro-to-simpsons-paradox/</guid><pubDate>Fri, 15 Sep 2017 05:57:28 GMT</pubDate></item><item><title>Into the Woods: Visualizing Random Forests with R</title><link>http://joshuahernandezblog.com/blog/Into%20the%20Woods/into-the-woods-visualizing-random-forests-with-r/</link><dc:creator>Joshua Hernandez</dc:creator><description>&lt;div tabindex="-1" id="notebook" class="border-box-sizing"&gt;
    &lt;div class="container" id="notebook-container"&gt;

&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;
&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;&lt;i&gt; You've probably heard random forest models described as "black boxes," models that show an input and an output and nothing in between. In this post, we go over techniques to show what a random forest model is doing, to make it less of a black box. &lt;/i&gt;&lt;/p&gt;
&lt;p&gt;&lt;a href="http://joshuahernandezblog.com/blog/Into%20the%20Woods/into-the-woods-visualizing-random-forests-with-r/"&gt;Read more…&lt;/a&gt; (3 min remaining to read)&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</description><category>Machine Learning</category><category>Quick Post</category><category>Random Forest</category><guid>http://joshuahernandezblog.com/blog/Into%20the%20Woods/into-the-woods-visualizing-random-forests-with-r/</guid><pubDate>Mon, 21 Aug 2017 13:48:17 GMT</pubDate></item></channel></rss>