At Strata+Hadoop World “R Day” Tutorial, Tuesday, March 29 2016, San Jose, California we spent some time on classifier measures derived from the so-called “confusion matrix.” We repeated our usual admonition to not use “accuracy itself” as a project quality goal (business people tend to ask for it as it […]
One of the things I like about R is: because it is not used for systems programming you can expect to install your own current version of R without interference from some system version of R that is deliberately being held back at some older version (for reasons of script […]
As John mentioned in his last post, we have been quite interested in the recent study by Fernandez-Delgado, et.al., “Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?” (the “DWN study” for short), which evaluated 179 popular implementations of common classification algorithms over 120 or so data […]
In most of our data science teaching (including our book Practical Data Science with R) we emphasize the deliberately easy problem of “exchangeable prediction.” We define exchangeable prediction as: given a series of observations with two distinguished classes of variables/observations denoted “x”s (denoting control variables, independent variables, experimental variables, or […]
Two of the most common methods of statistical inference are frequentism and Bayesianism (see Bayesian and Frequentist Approaches: Ask the Right Question for some good discussion). In both cases we are attempting to perform reliable inference of unknown quantities from related observations. And in both cases inference is made possible […]
I was watching my cousins play Unspeakable Words over Christmas break and got interested in the end game. The game starts out as a spell a word from cards and then bet some points game, but in the end (when you are down to one marker) it becomes a pure […]