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4 changes: 3 additions & 1 deletion _posts/2018-04-29-ROC-space-and-AUC.md
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Expand Up @@ -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
Expand Down Expand Up @@ -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
* *An introduction to ROC analysis* by Tom Fawcett
6 changes: 4 additions & 2 deletions _site/ROC-space-and-AUC.html
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<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">
Expand Down Expand Up @@ -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>
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8 changes: 5 additions & 3 deletions _site/feed.xml
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<?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;
Expand Down Expand Up @@ -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;
Expand Down Expand Up @@ -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>
&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>
6 changes: 4 additions & 2 deletions _site/page2/index.html
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Expand Up @@ -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>


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2 changes: 1 addition & 1 deletion _site/rss-feed.xml
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Expand Up @@ -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>

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</url>
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<lastmod>2019-09-12T00:31:53-07:00</lastmod>
<lastmod>2019-10-09T12:47:33-07:00</lastmod>
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