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R has something of a reputation for generating, shall we say, obscure error messages like this:

There are plenty of great domino cascades on YouTube, but this is one of the best. There are a couple of obvious cuts, but using multiple takes is more than made up for by the perfect coordination with the A-Trak tune. Enjoy:


That's all for this week. See you again on Monday!

Thanks to all who attended my webinar earlier this week, Reproducibility with Revolution R Open and the Checkpoint Package. If you missed the live session, you can catch up with the slides and video replay which I've embedded below.

Since 2007, Karl Rexer has been collecting data on the tools, skills and practices of statisticians and data miners. Over the years, his semi-annual Data Miner Survey has expanded in scope, and now includes research on the topics of data science, big data, and analytics applications on business.

by Herman Jopia

What is Binning?

Binning is the term used in scoring modeling for what is also known in Machine Learning as Discretization, the process of transforming a continuous characteristic into a finite number of intervals (the bins), which allows for a better understanding of its distribution and its relationship with a binary variable. The bins generated by the this process will eventually become the attributes of a predictive characteristic, the key component of a Scorecard.

Why Binning?

A quick heads-up that tomorrow (Tuesday) at 10AM Pacific Time I'll be giving a live (and free) webinar: Reproducibility with Revolution R Open and the Checkpoint Package.

Think of a number. Now multiply it with itself. Now add the number you first thought of. Multiply the result with itself. Again add the number you first thought of. Keep repeating that process. If the number you have keeps getting larger and larger, the number you first thought of is not part of the Mandelbrot Set. (For example, starting with 0.5 you get 0.75 -> 1.0625 -> 1.628 -> 3.153 ... and so on off to infinity, so 0.5 is not part of the Mandelbrot Set.)