<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>imurray</author><text>If you mainly care about prediction, rather than inspecting the fitted parameters, a lot of this detail is usually overkill.&lt;p&gt;To generalize well, it&amp;#x27;s almost always a good idea to have some sort of regularization, such as penalizing the sum of the square of the parameters. The extra term in the cost function will usually make the naive &amp;quot;normal equations&amp;quot; approach work fine, and give much the same predictions as fancy pivoted QR approaches. On my machine it&amp;#x27;s also a lot faster (the ball-park is ~~10x faster for large systems).&lt;p&gt;I&amp;#x27;m glad R has super-solid robust GLM implementations. And unless you&amp;#x27;re fitting &lt;i&gt;many&lt;/i&gt; models, you should probably just use such a library routine. However, I wish more tutorials and textbooks would spend more time on the reasons for numerical stability, and when one should care, rather than pushing that detail off into a trail of citations.</text><parent_chain></parent_chain></comment><story><title>Generalized linear models, abridged</title><url>http://bwlewis.github.io/GLM/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>rrmm</author><text>Nice overview.  I thought it was a bit odd that they pulled out a stieltjes integral so early on to define the mean and variance (I = Integral x dF(x)).  I wouldn&amp;#x27;t expect most people reading this sort of introductory material to be familiar with it.&lt;p&gt;Did it trip anyone up?</text><parent_chain></parent_chain></comment><story><title>Generalized linear models, abridged</title><url>http://bwlewis.github.io/GLM/</url></story>