The Pleasure of Finding Things Out: A blog by James Triveri
In this post, we’ll walk through the setup of an application server on a RHEL9 virtual machine to host multiple Dash and Shiny applications. While I found several guides that covered 60-70% of what I was looking to do, none fully matched what I needed. This setup checks all the boxes: Runs on a...
Carlisle Rainey
Background on arguing for a negligible effect I remember sitting in a talk while I was a graduate student, and the speaker said something like: “I expect no effect here, and, just as I expected, the difference is not statistically significant.” Of course, that’s not a compelling argument for a...
Carlisle Rainey
Firth’s Logit I like Firth’s logistic regression model (Firth 1993). I talk about that in Rainey and McCaskey (2021) and this Twitter thread. Kosmidis and Firth (2021) offer an excellent, recent follow-up as well. I’ll refer you to the papers for a careful discussion of the benefits, but Firth’s...
Carlisle Rainey
A Paper This post turned out to be somewhat popular, so I’ve written up a more formal, careful description of the idea in a full-length paper. You can find the preprint “Power Rules” here. Background I’ve wrapped up the argument that you should pursue statistical power in your experiments....
Carlisle Rainey
Background When I give students formula for confidence intervals, I find that students don’t have a sharp concept of how those confidence intervals work—even if I explain the components of the formula well. Even though they understand—seemingly very well—that the point estimate is noisy, they...
Carlisle Rainey
A Paper This post turned out to be somewhat popular, so I’ve written up a more formal, careful description of the idea in a full-length paper. You can find the preprint “Power Rules” here. Background In this post, I address confidence intervals that are nestled right up against zero.1 These...
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Preamble Catalan Castellers are collaborating (Wikipedia) Availability of distributed code tracking tools and associated collaborative tools make life much easier in building collaborative scientific tools and products. This is now especially much more important in data science as it is...
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Preamble Ebbinghaus (WikipediaMachine Learning (ML) is now a de-facto skill for every quantitative job and almost every industry embraced it, even though fundamentals of the field is not new at all. However, what does it mean to teach to a machine? Unfortunately, for even moderate technical people...
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Kindly reposted to KDnuggets by Gregory Piatetsky-Shapiro with the title Understanding overfitting: an inaccurate meme in machine learning Preamble There is a lot of confusion among practitioners regarding the concept of overfitting. It seems like, a kind of an urban legend or a meme, a folklore...