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Is 10,000 Cells Big?

Trick question: is a 10,000 cell numeric data.frame big or small? In the era of “big data” 10,000 cells is minuscule. Such data could be fit on fewer than 1,000 punched cards (or less than half a box). The joking answer is: it is small when they are selling you […]

Datashader is a big deal

I recently got back from Strata West 2017 (where I ran a very well received workshop on R and Spark). One thing that really stood out for me at the exhibition hall was Bokeh plus datashader from Continuum Analytics. I had the privilege of having Peter Wang himself demonstrate datashader […]

A budget of classifier evaluation measures

Beginning analysts and data scientists often ask: “how does one remember and master the seemingly endless number of classifier metrics?” My concrete advice is: Read Nina Zumel’s excellent series on scoring classifiers. Keep notes. Settle on one or two metrics as you move project to project. We prefer “AUC” early […]

A bit on the F1 score floor

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 […]

Thumbs up for Anaconda

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 […]

The Geometry of Classifiers

As John mentioned in his last post, we have been quite interested in the recent study by Fernandez-Delgado,, “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 […]

Can we try to make an adjustment?

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 […]

Frequentist inference only seems easy

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 […]