From 9dd97bc977935bae9db385c29283b75f5b694888 Mon Sep 17 00:00:00 2001
From: kevin lui <kevinywlui@gmail.com>
Date: Wed, 9 Oct 2019 12:52:14 -0700
Subject: [PATCH] Kevin's silly PR

---
 _posts/2018-04-29-ROC-space-and-AUC.md | 4 +++-
 _site/ROC-space-and-AUC.html           | 6 ++++--
 _site/feed.xml                         | 8 +++++---
 _site/page2/index.html                 | 6 ++++--
 _site/rss-feed.xml                     | 2 +-
 _site/sitemap.xml                      | 4 ++--
 6 files changed, 19 insertions(+), 11 deletions(-)

diff --git a/_posts/2018-04-29-ROC-space-and-AUC.md b/_posts/2018-04-29-ROC-space-and-AUC.md
index 0c421f0..72809ab 100755
--- a/_posts/2018-04-29-ROC-space-and-AUC.md
+++ b/_posts/2018-04-29-ROC-space-and-AUC.md
@@ -7,6 +7,8 @@ tags: [AUC, false positive rate, ROC, true positive rate, type I error, type II
 image: cost_roc_space.png
 ---
 
+# KEVIN WAS HERE
+
 Before discussing ROC curves and AUC, let's fix some terminology around the confusion matrix:
 
 * **Condition positive (negative):** real positive (negative) case in the data
@@ -70,4 +72,4 @@ Given any two classifiers in ROC space, we can interpolate on the line segment b
 
 ## References
 * *Measuring classifier performance: a coherent alternative to the area under the ROC curve* by David J. Hand
-* *An introduction to ROC analysis* by Tom Fawcett
\ No newline at end of file
+* *An introduction to ROC analysis* by Tom Fawcett
diff --git a/_site/ROC-space-and-AUC.html b/_site/ROC-space-and-AUC.html
index ff47aa8..433c983 100644
--- a/_site/ROC-space-and-AUC.html
+++ b/_site/ROC-space-and-AUC.html
@@ -43,8 +43,8 @@
 
 <meta name="author" content="Scott Roy"/>  
 <meta property="og:locale" content="en_US">
-<meta property="og:description" content="Before discussing ROC curves and AUC, let’s fix some terminology around the confusion matrix: Condition positive (negative): real positive (negative) case in the data True positive (negative): condition positive (negative)...">
-<meta property="description" content="Before discussing ROC curves and AUC, let’s fix some terminology around the confusion matrix: Condition positive (negative): real positive (negative) case in the data True positive (negative): condition positive (negative)...">
+<meta property="og:description" content="KEVIN WAS HERE Before discussing ROC curves and AUC, let’s fix some terminology around the confusion matrix: Condition positive (negative): real positive (negative) case in the data True positive (negative):...">
+<meta property="description" content="KEVIN WAS HERE Before discussing ROC curves and AUC, let’s fix some terminology around the confusion matrix: Condition positive (negative): real positive (negative) case in the data True positive (negative):...">
 <meta property="og:title" content="ROC space and AUC">
 <meta property="og:site_name" content="statsandstuff">
 <meta property="og:type" content="article">
@@ -114,6 +114,8 @@ <h1>
   <img src="/assets/img/cost_roc_space.png">
 
 
+<h1 id="kevin-was-here">KEVIN WAS HERE</h1>
+
 <p>Before discussing ROC curves and AUC, let’s fix some terminology around the confusion matrix:</p>
 
 <ul>
diff --git a/_site/feed.xml b/_site/feed.xml
index 60c2430..ac6050c 100644
--- a/_site/feed.xml
+++ b/_site/feed.xml
@@ -1,4 +1,4 @@
-<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.8.6">Jekyll</generator><link href="http://localhost:4000/feed.xml" rel="self" type="application/atom+xml" /><link href="http://localhost:4000/" rel="alternate" type="text/html" /><updated>2019-09-29T11:38:15-07:00</updated><id>http://localhost:4000/feed.xml</id><title type="html">statsandstuff</title><subtitle>a blog on statistics and machine learning</subtitle><author><name>Scott Roy</name></author><entry><title type="html">What makes a better score distribution?</title><link href="http://localhost:4000/what-makes-a-better-score-distribution.html" rel="alternate" type="text/html" title="What makes a better score distribution?" /><published>2019-09-29T00:00:00-07:00</published><updated>2019-09-29T00:00:00-07:00</updated><id>http://localhost:4000/what-makes-a-better-score-distribution</id><content type="html" xml:base="http://localhost:4000/what-makes-a-better-score-distribution.html">&lt;p&gt;Suppose I train two binary classifiers on some data, and after examining the score distributions of each, I see the results below.  Which score distribution is better?  (And by extension, which classifier is better?)&lt;/p&gt;
+<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.8.6">Jekyll</generator><link href="http://localhost:4000/feed.xml" rel="self" type="application/atom+xml" /><link href="http://localhost:4000/" rel="alternate" type="text/html" /><updated>2019-10-09T12:51:33-07:00</updated><id>http://localhost:4000/feed.xml</id><title type="html">statsandstuff</title><subtitle>a blog on statistics and machine learning</subtitle><author><name>Scott Roy</name></author><entry><title type="html">What makes a better score distribution?</title><link href="http://localhost:4000/what-makes-a-better-score-distribution.html" rel="alternate" type="text/html" title="What makes a better score distribution?" /><published>2019-09-29T00:00:00-07:00</published><updated>2019-09-29T00:00:00-07:00</updated><id>http://localhost:4000/what-makes-a-better-score-distribution</id><content type="html" xml:base="http://localhost:4000/what-makes-a-better-score-distribution.html">&lt;p&gt;Suppose I train two binary classifiers on some data, and after examining the score distributions of each, I see the results below.  Which score distribution is better?  (And by extension, which classifier is better?)&lt;/p&gt;
 
 &lt;table&gt;
   &lt;tbody&gt;
@@ -1456,7 +1456,9 @@ F &amp;= \frac{\text{SS}_{\text{reg}} / k}{\text{RSS} / (n-k-1)} \\
 &lt;h2 id=&quot;references&quot;&gt;References&lt;/h2&gt;
 &lt;ul&gt;
   &lt;li&gt;&lt;em&gt;Adaptive Rejection Sampling for Gibbs Sampling&lt;/em&gt; by W. R. Gilks and P. Wild&lt;/li&gt;
-&lt;/ul&gt;</content><author><name>Scott Roy</name></author><category term="adaptive rejection sampling" /><category term="rejection method" /><summary type="html">There is almost no difference between knowing a distribution’s density (and thus knowing its mean, variance, mode, or anything else about it) and being able to sample from the distribution.  On the one hand, if we can sample from a distribution, we can estimate the density with a histogram or kernel density estimator.  Conversely, I’ll discuss ways to sample from a density in this post.</summary><media:thumbnail xmlns:media="http://search.yahoo.com/mrss/" url="http://localhost:4000/sampling.png" /></entry><entry><title type="html">ROC space and AUC</title><link href="http://localhost:4000/ROC-space-and-AUC.html" rel="alternate" type="text/html" title="ROC space and AUC" /><published>2018-04-29T00:00:00-07:00</published><updated>2018-04-29T00:00:00-07:00</updated><id>http://localhost:4000/ROC-space-and-AUC</id><content type="html" xml:base="http://localhost:4000/ROC-space-and-AUC.html">&lt;p&gt;Before discussing ROC curves and AUC, let’s fix some terminology around the confusion matrix:&lt;/p&gt;
+&lt;/ul&gt;</content><author><name>Scott Roy</name></author><category term="adaptive rejection sampling" /><category term="rejection method" /><summary type="html">There is almost no difference between knowing a distribution’s density (and thus knowing its mean, variance, mode, or anything else about it) and being able to sample from the distribution.  On the one hand, if we can sample from a distribution, we can estimate the density with a histogram or kernel density estimator.  Conversely, I’ll discuss ways to sample from a density in this post.</summary><media:thumbnail xmlns:media="http://search.yahoo.com/mrss/" url="http://localhost:4000/sampling.png" /></entry><entry><title type="html">ROC space and AUC</title><link href="http://localhost:4000/ROC-space-and-AUC.html" rel="alternate" type="text/html" title="ROC space and AUC" /><published>2018-04-29T00:00:00-07:00</published><updated>2018-04-29T00:00:00-07:00</updated><id>http://localhost:4000/ROC-space-and-AUC</id><content type="html" xml:base="http://localhost:4000/ROC-space-and-AUC.html">&lt;h1 id=&quot;kevin-was-here&quot;&gt;KEVIN WAS HERE&lt;/h1&gt;
+
+&lt;p&gt;Before discussing ROC curves and AUC, let’s fix some terminology around the confusion matrix:&lt;/p&gt;
 
 &lt;ul&gt;
   &lt;li&gt;&lt;strong&gt;Condition positive (negative):&lt;/strong&gt; real positive (negative) case in the data&lt;/li&gt;
@@ -1524,4 +1526,4 @@ Let &lt;script type=&quot;math/tex&quot;&gt;f_{+}(x)&lt;/script&gt; be the score
 &lt;ul&gt;
   &lt;li&gt;&lt;em&gt;Measuring classifier performance: a coherent alternative to the area under the ROC curve&lt;/em&gt; by David J. Hand&lt;/li&gt;
   &lt;li&gt;&lt;em&gt;An introduction to ROC analysis&lt;/em&gt; by Tom Fawcett&lt;/li&gt;
-&lt;/ul&gt;</content><author><name>Scott Roy</name></author><category term="AUC" /><category term="false positive rate" /><category term="ROC" /><category term="true positive rate" /><category term="type I error" /><category term="type II error" /><summary type="html">Before discussing ROC curves and AUC, let’s fix some terminology around the confusion matrix:</summary><media:thumbnail xmlns:media="http://search.yahoo.com/mrss/" url="http://localhost:4000/cost_roc_space.png" /></entry></feed>
\ No newline at end of file
+&lt;/ul&gt;</content><author><name>Scott Roy</name></author><category term="AUC" /><category term="false positive rate" /><category term="ROC" /><category term="true positive rate" /><category term="type I error" /><category term="type II error" /><summary type="html">KEVIN WAS HERE</summary><media:thumbnail xmlns:media="http://search.yahoo.com/mrss/" url="http://localhost:4000/cost_roc_space.png" /></entry></feed>
\ No newline at end of file
diff --git a/_site/page2/index.html b/_site/page2/index.html
index a261e69..005c184 100644
--- a/_site/page2/index.html
+++ b/_site/page2/index.html
@@ -214,12 +214,14 @@ <h1>
   </div>
   
   <p>
-    Before discussing ROC curves and AUC, let’s fix some terminology around the confusion matrix:
+    KEVIN WAS HERE
+
+Before discussing ROC curves and AUC, let’s fix some terminology around the confusion matrix:
 
 
   Condition positive (negative): real positive (negative) case in the data
   True positive (negative): condition positive (negative) that is classified as positive (negative)
-  False positive (negative): condition negative (positive) that is classifie... <a href="/ROC-space-and-AUC.html">Read more</a>
+  False positive (negative): condition negative (positive) t... <a href="/ROC-space-and-AUC.html">Read more</a>
     <span class="post-date"><i class="fa fa-calendar" aria-hidden="true"></i> 29 Apr 2018 - <i class="fa fa-clock-o" aria-hidden="true"></i> 
   
   
diff --git a/_site/rss-feed.xml b/_site/rss-feed.xml
index 1e24f4b..476a37d 100644
--- a/_site/rss-feed.xml
+++ b/_site/rss-feed.xml
@@ -149,7 +149,7 @@ There are many ways we could interpret “most related”:&lt;/p&gt;
 					<dc:creator>Scott Roy</dc:creator>
 				
 				
-					<description>&lt;p&gt;Before discussing ROC curves and AUC, let’s fix some terminology around the confusion matrix:&lt;/p&gt;
+					<description>&lt;h1 id=&quot;kevin-was-here&quot;&gt;KEVIN WAS HERE&lt;/h1&gt;
 
 </description>
 				
diff --git a/_site/sitemap.xml b/_site/sitemap.xml
index deb8cc2..282e9a6 100644
--- a/_site/sitemap.xml
+++ b/_site/sitemap.xml
@@ -103,10 +103,10 @@
 </url>
 <url>
 <loc>http://localhost:4000/assets/img/backprop_params.pdf</loc>
-<lastmod>2019-09-12T00:31:53-07:00</lastmod>
+<lastmod>2019-10-09T12:47:33-07:00</lastmod>
 </url>
 <url>
 <loc>http://localhost:4000/assets/img/backprop_prevoutput.pdf</loc>
-<lastmod>2019-09-12T00:47:56-07:00</lastmod>
+<lastmod>2019-10-09T12:47:33-07:00</lastmod>
 </url>
 </urlset>