<story><title>Machine Learning for Everyday Tasks</title><url>http://blog.mailgun.com/machine-learning-for-everyday-tasks/</url></story><parent_chain></parent_chain><comment><author>minimaxir</author><text>This is a case where knowing the data and it&amp;#x27;s &lt;i&gt;inherent structure&lt;/i&gt; is just as important, if not more important, then knowing how to apply machine learning.&lt;p&gt;In this case, the formula was (time ~ content length + number of tags). Except, by construction, the content length is &lt;i&gt;positively correlated&lt;/i&gt; with the number of tags! And since the parsing tags is the primary purpose of HTML scrapers, it&amp;#x27;s likely that content length is completely redundant or a poor predictor of time. (which was the conclusion). If you wanted to calculate the maginitude&amp;#x2F;significance of an explanatory variable, a linear regression is more than sufficient, and R has great tools for that.&lt;p&gt;The quantile plot revealed there is a skew in time processed, presumably the number of tags has a similar skew.</text></comment>
<story><title>Machine Learning for Everyday Tasks</title><url>http://blog.mailgun.com/machine-learning-for-everyday-tasks/</url></story><parent_chain></parent_chain><comment><author>nacc</author><text>I might have missed something, but if they only have 2 features, simply plotting the data out will make any trend very clear.</text></comment>