We have been recently working on and presenting on nested modeling issues. These are situations where the output of one trained machine learning model is part of the input of a later model or procedure. I am now of the opinion that correct treatment of nested models is one of the biggest opportunities for improvement in data science practice. Nested models can be more powerful than non-nested, but are easy to get wrong.
We are pleased to release a new free data science video lecture: Debugging R code using R, RStudio and wrapper functions. In this 8 minute video we demonstrate the incredible power of R using wrapper functions to catch errors for later reproduction and debugging. If you haven’t tried these techniques this will really improve your debugging game.
All code and examples can be found here and in WVPlots. Continue reading Free data science video lecture: debugging in R
We are pleased to announce our book Practical Data Science with R (Nina Zumel, John Mount, Manning 2014) is part of Manning’s “Deal of the Day” of April 9th 2016. This one day only offer gets you half off for physical book (with free e-copy) or paid e-copy (e-copy simultaneous pdf + ePub + kindle, and DRM free!).
Here is the discount count in Tweetable form (please Tweet/share!):
Deal of the Day April 9: Half off my book Practical Data Science with R. Use code
In celebration of this we are offering our video instruction course Introduction to Data Science (Nina Zumel, John Mount 2015) is also half off with “code
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 is the word they are most familiar with, but it usually isn’t what they really want).
One reason not to use accuracy: an example where a classifier that does nothing is “more accurate” than one that actually has some utility. (Figure credit Nina Zumel, slides here)
And we worked through the usual bestiary of other metrics (precision, recall, sensitivity, specificity, AUC, balanced accuracy, and many more).
Please read on to see what stood out. Continue reading A bit on the F1 score floor
Nina Zumel and I have been working on packaging our favorite graphing techniques in a more reusable way that emphasizes the analysis task at hand over the steps needed to produce a good visualization. The idea is: we sacrifice some of the flexibility and composability inherent to ggplot2 in R for a menu of prescribed presentation solutions (which we are sharing on Github).
For example the plot below showing both an observed discrete empirical distribution (as stems) and a matching theoretical distribution (as bars) is a built in “one liner.”
Please read on for some of the ideas and how to use this package. Continue reading WVPlots: example plots in R using ggplot2