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	<title>Comments on: What is a good classification accuracy in data mining?</title>
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	<link>http://www.dataminingblog.com/what-is-a-good-classification-accuracy-in-data-mining/</link>
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		<title>By: kapil</title>
		<link>http://www.dataminingblog.com/what-is-a-good-classification-accuracy-in-data-mining/comment-page-1/#comment-124547</link>
		<dc:creator>kapil</dc:creator>
		<pubDate>Tue, 17 Jan 2012 07:34:19 +0000</pubDate>
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		<description>Great Article regarding Classification Accuracy. I want to add/suggest something useful formula in this...

Classification Accuracy = Number of correctly classified testig examples/ Number Of testing Examples</description>
		<content:encoded><![CDATA[<p>Great Article regarding Classification Accuracy. I want to add/suggest something useful formula in this&#8230;</p>
<p>Classification Accuracy = Number of correctly classified testig examples/ Number Of testing Examples</p>
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	<item>
		<title>By: Priya</title>
		<link>http://www.dataminingblog.com/what-is-a-good-classification-accuracy-in-data-mining/comment-page-1/#comment-105744</link>
		<dc:creator>Priya</dc:creator>
		<pubDate>Tue, 05 Apr 2011 02:46:22 +0000</pubDate>
		<guid isPermaLink="false">http://www.dataminingblog.com/?p=961#comment-105744</guid>
		<description>Sir,
I am doing my PG project related to credit scoring.

I am obtaining the accuracy rate of around 70%
and the precision rate of around 85%

Is it the good classification range.

Thankyou</description>
		<content:encoded><![CDATA[<p>Sir,<br />
I am doing my PG project related to credit scoring.</p>
<p>I am obtaining the accuracy rate of around 70%<br />
and the precision rate of around 85%</p>
<p>Is it the good classification range.</p>
<p>Thankyou</p>
]]></content:encoded>
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	<item>
		<title>By: Sandro Saitta</title>
		<link>http://www.dataminingblog.com/what-is-a-good-classification-accuracy-in-data-mining/comment-page-1/#comment-77144</link>
		<dc:creator>Sandro Saitta</dc:creator>
		<pubDate>Tue, 27 Apr 2010 21:25:18 +0000</pubDate>
		<guid isPermaLink="false">http://www.dataminingblog.com/?p=961#comment-77144</guid>
		<description>@Jeff: Thanks for your comment. Point 1) is particularly true in the online advertising domain where the click is a &quot;rare&quot; event.</description>
		<content:encoded><![CDATA[<p>@Jeff: Thanks for your comment. Point 1) is particularly true in the online advertising domain where the click is a &#8220;rare&#8221; event.</p>
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	<item>
		<title>By: Jeff</title>
		<link>http://www.dataminingblog.com/what-is-a-good-classification-accuracy-in-data-mining/comment-page-1/#comment-76972</link>
		<dc:creator>Jeff</dc:creator>
		<pubDate>Mon, 19 Apr 2010 23:40:15 +0000</pubDate>
		<guid isPermaLink="false">http://www.dataminingblog.com/?p=961#comment-76972</guid>
		<description>Hey Sandro, Interesting post. I think that this is one of the challenges I have faced when presenting a model (results) to a non technical audience. &quot;You were only correct 70% of the time!?&quot;. You do need to explain the domain, the lift and the profit (if applicable) that comes from that improvement immediately when showing the accuracy.

I would add a couple additional thoughts:

1) Just accuracy alone is not always enough - think of a *rare* event where prediction of &#039;false&#039; is nearly always correct - but not useful. There, true positives, false positives etc. become key

2) In some domains, like database marketing, also with a rare event, you are less interested in classification accuracy per say as in the predicted probability and the lift of top deciles.

My 2 cents
Jeff</description>
		<content:encoded><![CDATA[<p>Hey Sandro, Interesting post. I think that this is one of the challenges I have faced when presenting a model (results) to a non technical audience. &#8220;You were only correct 70% of the time!?&#8221;. You do need to explain the domain, the lift and the profit (if applicable) that comes from that improvement immediately when showing the accuracy.</p>
<p>I would add a couple additional thoughts:</p>
<p>1) Just accuracy alone is not always enough &#8211; think of a *rare* event where prediction of &#8216;false&#8217; is nearly always correct &#8211; but not useful. There, true positives, false positives etc. become key</p>
<p>2) In some domains, like database marketing, also with a rare event, you are less interested in classification accuracy per say as in the predicted probability and the lift of top deciles.</p>
<p>My 2 cents<br />
Jeff</p>
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