Nina Zumel recently mentioned the use of Laplace noise in “count codes” by Misha Bilenko (see here and here) as a known method to break the overfit bias that comes from using the same data to design impact codes and fit a next level model. It is a fascinating method inspired by differential privacy methods, that Nina and I respect but don’t actually use in production.
Nested dolls, Wikimedia Commons
Please read on for my discussion of some of the limitations of the technique, and how we solve the problem for impact coding (also called “effects codes”), and a worked example in R. Continue reading Laplace noising versus simulated out of sample methods (cross frames)
Nina Zumel recently announced upcoming speaking appearances. I want to promote the upcoming sessions at ODSC West 2016 (11:15am-1:00pm on Friday November 4th, or 3:00pm-4:30pm on Saturday November 5th) and invite executives, managers, and other data science consumers to attend. We assume most of the Win-Vector blog audience is made of practitioners (who we hope are already planning to attend), so we are asking you our technical readers to help promote this talk to a broader audience of executives and managers.
Our messages is: if you have to manage data science projects, you need to know how to evaluate results.
In these talks we will lay out how data science results should be examined and evaluated. If you can’t make ODSC (or do attend and like what you see), please reach out to us and we can arrange to present an appropriate targeted summarized version to your executive team. Continue reading Data science for executives and managers
Recently saw a really fun article making the rounds: “The prevalence of statistical reporting errors in psychology (1985–2013)”, Nuijten, M.B., Hartgerink, C.H.J., van Assen, M.A.L.M. et al., Behav Res (2015), doi:10.3758/s13428-015-0664-2. The authors built an R package to check psychology papers for statistical errors. Please read on for how that is possible, some tools, and commentary.
Early automated analysis:
Trial model of a part of the Analytical Engine, built by Babbage, as displayed at the Science Museum (London) (Wikipedia).
Continue reading Proofing statistics in papers
Nina Zumel prepared an excellent article on the consequences of working with relative error distributed quantities (such as wealth, income, sales, and many more) called “Living in A Lognormal World.” The article emphasizes that if you are dealing with such quantities you are already seeing effects of relative error distributions (so it isn’t an exotic idea you bring to analysis, it is a likely fact about the world that comes at you). The article is a good example of how to plot and reason about such situations.
I am just going to add a few additional references (mostly from Nina) and some more discussion on log-normal distributions versus Zipf-style distributions or Pareto distributions. Continue reading Relative error distributions, without the heavy tail theatrics
I just got back from a very good conference organized by startup.ml: Adversarial Machine Learning. Please read on for my to comments on part of one of the very good talks. Continue reading Adversarial machine learning
Writing a book is a sacrifice. It takes a lot of time, represents a lot of missed opportunities, and does not (directly) pay very well. If you do a good job it may pay back in good-will, but producing a serious book is a great challenge.
Nina Zumel and I definitely troubled over possibilities for some time before deciding to write Practical Data Science with R, Nina Zumel, John Mount, Manning 2014.
In the end we worked very hard to organize and share a lot of good material in what we feel is a very readable manner. But I think the first-author may have been signaling and preparing a bit earlier than I was aware we were writing a book. Please read on to see some of her prefiguring work. Continue reading Did she know we were writing a book?
I want to recommend an excellent article on the recent claimed use of differential privacy to actually preserve user privacy: “A Few Thoughts on Cryptographic Engineering” by Matthew Green.
After reading the article we have a few follow-up thoughts on the topic. Continue reading Another note on differential privacy
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.
Continue reading On Nested Models
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
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