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 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
Win-Vector LLC will be presenting on statistically validating models using R and data science at:
- Strata+Hadoop World “R Day” Tutorial 9:00am–5:00pm Tuesday, March 29 2016, San Jose, California.
- ODSC San Francisco Meetup, 6:30pm-9:00pm Thursday, March 31, 2016, San Francisco, California.
We will share code and examples.
Registration required (and Strata is a paid conference). Please Tweet/forward. We hope to see you soon!
The Microsoft Data Science User Group just sponsored Dr. Nina Zumel‘s presentation “Preparing Data for Analysis Using R”. Microsoft saw Win-Vector LLC‘s ODSC West 2015 presentation “Prepping Data for Analysis using R” and generously offered to sponsor improving it and disseminating it to a wider audience.
We feel Nina really hit the ball out of the park with over 400 new live viewers. Read more for links to even more free materials! Continue reading More on preparing data
Win-Vector LLC has been offering a couple of online video courses on the topics of data science and A/B testing (both using R). These are high quality courses and well worth the money and time needed to work through them closely (with all materials distributed on GitHub).
Our current distributor is Udemy, which has just announced a unilateral change in pricing policy (March 2, 2016). This note is about the current status of these courses. Continue reading Win-Vector video courses: price/status changes
We at Win-Vector LLC are very proud to announce that RStudio just inducted two more of our demonstration Shiny applications into their Shiny User Showcase gallery. Continue reading More Shiny user showcase demonstrations
One of the trickier tasks in clustering is determining the appropriate number of clusters. Domain-specific knowledge is always best, when you have it, but there are a number of heuristics for getting at the likely number of clusters in your data. We cover a few of them in Chapter 8 (available as a free sample chapter) of our book Practical Data Science with R.
We also came upon another cool approach, in the
mixtools package for mixture model analysis. As with clustering, if you want to fit a mixture model (say, a mixture of gaussians) to your data, it helps to know how many components are in your mixture. The
boot.comp function estimates the number of components (let’s call it k) by incrementally testing the hypothesis that there are k+1 components against the null hypothesis that there are k components, via parametric bootstrap.
You can use a similar idea to estimate the number of clusters in a clustering problem, if you make a few assumptions about the shape of the clusters. This approach is only heuristic, and more ad-hoc in the clustering situation than it is in mixture modeling. Still, it’s another approach to add to your toolkit, and estimating the number of clusters via a variety of different heuristics isn’t a bad idea.