The Facebook data science blog shared some fun data explorations this Valentine’s Day in Carlos Greg Diuk’s “The Formation of Love”. They are rightly receiving positive interest in and positive reviews of their work (for example Robinson Meyer’s Atlantic article). The finding is also a great opportunity to discuss the gap between cool data mining results and usable predictive models. Data mining results like this (and the infamous “Beer and Diapers story”) face an expectation that one is immediately ready to implement something like what is claimed in: “Target Figured Out A Teen Girl Was Pregnant Before Her Father Did” once an association is plotted.
Producing a revenue improving predictive model is much harder than mining an interesting association. And this is what we will discuss here. Continue reading
Nina Zumel and I ( John Mount ) have been working very hard on producing an exciting new book called “Practical Data Science with R.” The book has now entered Manning Early Access Program (MEAP) which allows you to subscribe to chapters as they become available and give us feedback before the book goes into print.
Please subscribe to our book, your support now will help us improve it. Please also forward this offer to your friends and colleagues (and please ask them to also subscribe and forward). Continue reading
A fair complaint when seeing yet another “data science” article is to say: “this is just medical statistics” or “this is already part of bioinformatics.” We certainly label many articles as “data science” on this blog. Probably the complaint is slightly cleaner if phrased as “this is already known statistics.” But the essence of the complaint is a feeling of claiming novelty in putting old wine in new bottles. Rob Tibshirani nailed this type of distinction in is famous machine learning versus statistics glossary.
I’ve written about statistics v.s. machine learning , but I would like to explain why we (the authors of this blog) often use the term data science. Nina Zumel explained being a data scientist very well, I am going to take a swipe at explaining data science.
We (the authors on this blog) label many of our articles as being about data science because we want to emphasize that the various techniques we write about are only meaningful when considered parts of a larger end to end process. The process we are interested in is the deployment of useful data driven models into production. The important components are learning the true business needs (often by extensive partnership with customers), enabling the collection of data, managing data, applying modeling techniques and applying statistics criticisms. The pre-existing term I have found that is closest to describing this whole project system is data science, so that is the term I use. I tend to use it a lot, because while I love the tools and techniques our true loyalty is to the whole process (and I want to emphasize this to our readers).
The phrase “data science” as in use it today is a fairly new term (made popular by William S. Cleveland, DJ Patil, and Jeff Hammerbacher). I myself worked in a “computational sciences” group in the mid 1990’s (this group emphasized simulation based modeling of small molecules and their biological interactions, the naming was an attempt to emphasize computation over computers). So for me “data science” seems like a good term when your work is driven by data (versus driven from computer simulations). For some people data science is considered a new calling and for others it is a faddish misrepresentation of work that has already been done. I think there are enough substantial differences in approach between traditional statistics, machine learning, data mining, predictive analytics, and data science to justify at least this much nomenclature. In this article I will try to describe (but not fully defend) my opinion. Continue reading
Given the range of wants, diverse data sources, required innovation and methods it often feels like data science projects are immune to planning, scoping and tracking. Without a system to break a data science project into smaller observable components you greatly increase your risk of failure. As a followup to the statistical ideas we shared in setting expectations in data science projects we share a few project planning ideas from software engineering. Continue reading
A bit more on the ROC/AUC
The receiver operating characteristic curve (or ROC) is one of the standard methods to evaluate a scoring system. Nina Zumel has described its application, but we would like to emphasize out some additional details. In my opinion while the ROC is a useful tool, the “area under the curve” (AUC) summary often read off it is not as intuitive and interpretable as one would hope or some writers assert. Continue reading
Model level fit summaries can be tricky in R. A quick read of model fit summary data for factor levels can be misleading. We describe the issue and demonstrate techniques for dealing with them. Continue reading
One of the shortcomings of regression (both linear and logistic) is that it doesn’t handle categorical variables with a very large number of possible values (for example, postal codes). You can get around this, of course, by going to another modeling technique, such as Naive Bayes; however, you lose some of the advantages of regression — namely, the model’s explicit estimates of variables’ explanatory value, and explicit insight into and control of variable to variable dependence.
Here we discuss one modeling trick that allows us to keep categorical variables with a large number of values, and at the same time retain much of logistic regression’s power.
A primary problem data scientists face again and again is: how to properly adapt or treat variables so they are best possible components of a regression. Some analysts at this point delegate control to a shape choosing system like neural nets. I feel such a choice gives up far too much statistical rigor, transparency and control without real benefit in exchange. There are other, better, ways to solve the reshaping problem. A good rigorous way to treat variables are to try to find stabilizing transforms, introduce splines (parametric or non-parametric) or use generalized additive models. A practical or pragmatic approach we advise to get some of the piecewise reshaping power of splines or generalized additive models is: a modeling trick we call “masked variables.” This article works a quick example using masked variables. Continue reading
The design of the statistical programming language R sits in a slightly uncomfortable place between the functional programming and object oriented paradigms. The upside is you get a lot of the expressive power of both programming paradigms. A downside of this is: the not always useful variability of the language’s list and object extraction operators.
Towards the end of our write-up Survive R we recommended using explicit environments with
get() to simulate mutable string-keyed maps for storing results. This advice rose out of frustration with the apparent inconsistency with the user facing R list operators. In this article we bite the bullet and discuss the R list operators a bit more clearly. Continue reading
A big congratulations to Win-Vector LLC‘s Dr. Nina Zumel for authoring and teaching portions of EMC‘s new Data Science and Big Data Analytics training and certification program. A big congratulations to EMC, EMC Education Services and Greenplum for creating a great training course. Finally a huge thank you to EMC, EMC Education Services and Greenplum for inviting Win-Vector LLC to contribute to this great project.