Win-Vector Blog

Logistic regression is one of the most popular ways to fit models for categorical data, especially for binary response data. It is the most important (and probably most used) member of a class of models called generalized linear models. Unlike linear regression, logistic regression can directly predict probabilities (values that are restricted to the (0,1) interval); furthermore, those probabilities are well-calibrated when compared to the probabilities predicted by some other classifiers, such as Naive Bayes. Logistic regression preserves the marginal probabilities of the training data. The coefficients of the model also provide some hint of the relative importance of each input variable.

While you don’t have to know how to derive logistic regression or how to implement it in order to use it, the details of its derivation give important insights into interpreting and troubleshooting the resulting models. Unfortunately, most derivations (like the ones in [Agresti, 1990] or [Hastie, et.al, 2009]) are too terse for easy comprehension. Here, we give a derivation that is less terse (and less general than Agresti’s), and we’ll take the time to point out some details and useful facts that sometimes get lost in the discussion. Read more…

Programmers should definitely know how to use R. I don’t mean they should switch from their current language to R, but they should think of R as a handy tool during development. Read more…

One of the recurring frustrations in data analytics is that your data is never in the right shape. Worst case: you are not aware of this and every step you attempt is more expensive, less reliable and less informative than you would want. Best case: you notice this and have the tools to reshape your data.

There is no final “right shape.” In fact even your data is never right. You will always be called to re-do your analysis (new variables, new data, corrections) so you should always understand you are on your “penultimate analysis” (always one more to come). This is why we insist on using general methods and scripted techniques, as these methods are much much easier to reliably reapply on new data than GUI/WYSWYG techniques.

In this article we will work a small example and call out some R tools that make reshaping your data much easier. The idea is to think in terms of “relational algebra” (like SQL) and transform your data towards your tools (and not to attempt to adapt your tools towards the data in an ad-hoc manner). Read more…

With the well deserved popularity of A/B testing computer scientists are finally becoming practicing statisticians. One part of experiment design that has always been particularly hard to teach is how to pick the size of your sample. The two points that are hard to communicate are that:

• The required sample size is essentially independent of the total population size.
• The required sample size depends strongly on the strength of the effect you are trying to measure.

These things are only hard to explain because the literature is overly technical (too many buzzwords and too many irrelevant concerns) and these misapprehensions can’t be relieved unless you spend some time addressing the legitimate underlying concerns they are standing in for. As usual explanation requires common ground (moving to shared assumptions) not mere technical bullying.

We will try to work through these assumptions and then discuss proper sample size. Read more…

This is a tutorial on how to try out a new package in R. The summary is: expect errors, search out errors and don’t start with the built in examples or real data.

Suppose you want to try out a novel statistical technique? A good fraction of the time R is your best bet for a first trial. Take as an example general additive models (“Generalized Additive Models,” Trevor J Hastie, Robert Tibshirani, Statistical Science (1986) vol. 1 (3) pp. 297-318); R has a package named “gam” written by Trevor Hastie himself. But, like most R packages, trying the package from the supplied documentation brings in unfamiliar data and concerns. It is best to start small and quickly test if the package itself is suitable to your needs. We give a quick outline of how to learn such a package and quickly find out if the package is for you.

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We discuss a “medium scale data” technique that we call “SQL Screwdriver.”

Previously we discussed some of the issues of large scale data analytics. A lot of the work done at the MapReduce scale is necessarily limited to mere aggregation and report generation. But what of medium scale? That is data too large to perform all steps in your favorite tool (R, Excel or something else) but small enough that you are expected to produce sophisticated models, decisions and analysis. At this scale, if properly prepared, you don’t need large scale tools and their limitations. With extra preparation you can continue to use your preferred tools. We call this the realm of medium scale data and discuss a preparation tool style we call “screwdriver” (as opposed to larger hammers).

We stand the “no SQL” movement on its head and discuss the beneficial use of SQL without a server (as opposed to their vision of a key-value store without SQL). Database servers can be a nuisance- but that is not enough reason to give up the power of relational query languages.
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One of the current best tools in the machine learning toolbox is the 1930s statistical technique called logistic regression. We explain how to add professional quality logistic regression to your analytic repertoire and describe a bit beyond that. Read more…

We extend the ideas of from Automatic Differentiation with Scala to include the reverse accumulation. Reverse accumulation is a non-obvious improvement to automatic differentiation that can in many cases vastly speed up calculations of gradients. Read more…

This article is a worked-out exercise in applying the Scala type system to solve a small scale optimization problem. For this article we supply complete Scala source code (under a GPLv3 license) and some design discussion. Read more…

Having worked with Unix (BSD, HPUX, IRIX, Linux and OSX), Windows (NT4, 2000, XP, Vista and 7) for quite a while I have seen a lot of different software tools. I would like to quickly exhibit my “must have” list. These are the packages that I find to be the single “must have offerings” in a number of categories. I have avoided some categories (such as editors, email programs, programing language, IDEs, photo editors, backup solutions, databases, database tools and web tools) where I have no feeling of having seen a single absolute best offering.

The spirit of the list is to pick items such that: if you disagree with an item in this list then either you are wrong or you know something I would really like to hear about.

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