Blog Archives

The Bayesian approach to ridge regression

October 30, 2016
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The Bayesian approach to ridge regression

In a previous post, we demonstrated that ridge regression (a form of regularized linear regression that attempts to shrink the beta coefficients toward zero) can be super-effective at combating overfitting and lead to a greatly more generalizable model. This approach… Continue reading →

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Using Python decorators to be a lazy programmer: a case study

Using Python decorators to be a lazy programmer: a case study

Decorators are considered one of the more advanced features of python and it will often be the last topic in a python class or introductory book. It will, unfortunately, also be one that trips up many beginning or even intermediate… Continue reading →

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Genre-based Music Recommendations Using Open Data (and the problem with recommender systems)

January 12, 2016
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Genre-based Music Recommendations Using Open Data (and the problem with recommender systems)

After a long 12 months of pouring my soul into it, my book, Data Analysis with R, was finally published. After the requisite 2-4 day breather, I started thinking about how I was going to get back into the swing… Continue reading →

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Kickin’ it with elastic net regression

August 20, 2015
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Kickin’ it with elastic net regression

With the kind of data that I usually work with, overfitting regression models can be a huge problem if I'm not careful. Ridge regression is a really effective technique for thwarting overfitting. It does this by penalizing the L2 norm… Continue reading →

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Lessons learned in high-performance R

Lessons learned in high-performance R

On this blog, I've had a long running investigation/demonstration of how to make a "embarrassingly-parallel" but computationally intractable (on commodity hardware, at least) R problem more performant by using parallel computation and Rcpp. The example problem is to find the… Continue reading →

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The hardest thing about teaching statistics

April 30, 2015
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The hardest thing about teaching statistics

(Note: this post should probably be titled "Quantitative Methods of Curricula Planning" but I thought the current title would draw more interest–though they would both lose out to "These Weird Approaches To Lesson Planning Will Leave You Speechless") Suppose you… Continue reading →

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I’m all about that bootstrap (’bout that bootstrap)

March 21, 2015
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I’m all about that bootstrap (’bout that bootstrap)

As some of my regular readers may know, I'm in the middle of writing a book on introductory data analysis with R. I'm at the point in the writing of the book now where I have to make some hard… Continue reading →

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Playing around with #rstats twitter data

February 28, 2015
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Playing around with #rstats twitter data

As a bit of weekend fun, I decided to briefly look into the #rstats twitter data that Stephen Turner collected and made available (thanks!). Essentially, this data set contains some basic information about over 100,000 tweets that contain the hashtag… Continue reading →

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Assertive R programming in dplyr/magrittr pipelines

January 23, 2015
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Assertive R programming in dplyr/magrittr pipelines

A lot of my job–and side projects, for that matter–involve running R scripts on updates of open government data. While I’m infinitely grateful to have access to any interesting open datasets in the first place, I can’t ignore that dealing… Continue reading →

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What does Flatland have to do with Haskell?

December 23, 2014
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What does Flatland have to do with Haskell?

Edwin Abbott's 1884 novella, Flatland, recounts the misadventures of a square that lives in a two-dimensional world called "Flatland". In this story, the square has a dream where he visits a one-dimensional world (Lineland) and unsuccessfully tries to educate the… Continue reading →

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