Category Archives: Opinion

How sure are you that large margin implies low VC dimension?

How sure are you that large margin implies low VC dimension (and good generalization error)? It is true. But even if you have taken a good course on machine learning you many have seen the actual proof (with all of the caveats and conditions). I worked through the literature proofs over the holiday and it took a lot of notes to track what is really going on in the derivation of the support vector machine.

Figure: the standard SVM margin diagram, this time with some un-marked data added.
Continue reading How sure are you that large margin implies low VC dimension?

Random Test/Train Split is not Always Enough

Most data science projects are well served by a random test/train split. In our book Practical Data Science with R we strongly advise preparing data and including enough variables so that data is exchangeable, and scoring classifiers using a random test/train split.

With enough data and a big enough arsenal of methods, it’s relatively easy to find a classifier that looks good; the trick is finding one that is good. What many data science practitioners (and consumers) don’t seem to remember is that when evaluating a model, a random test/train split may not always be enough.

Continue reading Random Test/Train Split is not Always Enough

Excel spreadsheets are hard to get right

Any practicing data scientist is going to eventually have to work with a data stored in a Microsoft Excel spreadsheet. A lot of analysts use this format, so if you work with others you are going to run into it. We have already written how we don’t recommend using Excel-like formats to exchange data. But we know if you are going to work with others you are going to have to make accommodations (we even built our own modified version of gdata‘s underlying Perl script to work around a bug).

But one thing that continues to confound us is how hard it is to read Excel data correctly. When Excel exports into CSV/TSV style formats it uses fairly clever escaping rules about quotes and new-lines. Most CSV/TSV readers fail to correctly implement these rules and often fail on fields that contain actual quote characters, separators (tab or comma), or new-lines. Another issue is Excel itself often transforms data without any user verification or control. For example: Excel routinely turns date-like strings into time since epoch (which it then renders as a date). We recently ran into another uncontrollable Excel transform: changing the strings “TRUE” and “FALSE” into 1 and 0 inside the actual “.xlsx” file. That is Excel does not faithfully store the strings “TRUE” and “FALSE” even in its native format. Most Excel users do not know about this, so they certainly are in no position to warn you about it.

This would be a mere annoyance, except it turns out Libre Office (or at least LibreOffice_4.3.4_MacOS_x86-64) has a severe and silent data mangling bug on this surprising Microsoft boolean type.

We first ran into this in client data (and once the bug triggered it seemed to alter most of the columns), but it turns out the bug is very easy to trigger. In this note we will demonstrate the data representation issue and bug. Continue reading Excel spreadsheets are hard to get right

Bias/variance tradeoff as gamesmanship

Continuing our series of reading out loud from a single page of a statistics book we look at page 224 of the 1972 Dover edition of Leonard J. Savage’s “The Foundations of Statistics.” On this page we are treated to an example attributed to Leo A. Goodman in 1953 that illustrates how for normally distributed data the maximum likelihood, unbiased, and minimum variance estimators of variance are in fact typically three different values. So in the spirit of gamesmanship you always have at least two reasons to call anybody else’s estimator incorrect. Continue reading Bias/variance tradeoff as gamesmanship

Factors are not first-class citizens in R

The primary user-facing data types in the R statistical computing environment behave as vectors. That is: one dimensional arrays of scalar values that have a nice operational algebra. There are additional types (lists, data frames, matrices, environments, and so-on) but the most common data types are vectors. In fact vectors are so common in R that scalar values such as the number 5 are actually represented as length-1 vectors. We commonly think about working over vectors of “logical”, “integer”, “numeric”, “complex”, “character”, and “factor” types. However, a “factor” is not a R vector. In fact “factor” is not a first-class citizen in R, which can lead to some ugly bugs.

For example, consider the following R code.

levels <- c('a','b','c')
f <- factor(c('c','a','a',NA,'b','a'),levels=levels)
## [1] c    a    a    <NA> b    a   
## Levels: a b c
## [1] "factor"

This example encoding a series of 6 observations into a known set of factor-levels ('a', 'b', and 'c'). As is the case with real data some of the positions might be missing/invalid values such as NA. One of the strengths of R is we have a uniform explicit representation of bad values, so with appropriate domain knowledge we can find and fix such problems. Suppose we knew (by policy or domain experience) that the level 'a' was a suitable default value to use when the actual data is missing/invalid. You would think the following code would be the reasonable way to build a new revised data column.

fRevised <- ifelse(,'a',f)
##  [1] "3" "1" "1" "a" "2" "1"
## [1] "character"

Notice the new column fRevised is an absolute mess (and not even of class/type factor). This sort of fix would have worked if f had been a vector of characters or even a vector of integers, but for factors we get gibberish.

We are going to work through some more examples of this problem. Continue reading Factors are not first-class citizens in R

Reading the Gauss-Markov theorem

What is the Gauss-Markov theorem?

From “The Cambridge Dictionary of Statistics” B. S. Everitt, 2nd Edition:

A theorem that proves that if the error terms in a multiple regression have the same variance and are uncorrelated, then the estimators of the parameters in the model produced by least squares estimation are better (in the sense of having lower dispersion about the mean) than any other unbiased linear estimator.

This is pretty much considered the “big boy” reason least squares fitting can be considered a good implementation of linear regression.

Suppose you are building a model of the form:

    y(i) = B . x(i) + e(i)

where B is a vector (to be inferred), i is an index that runs over the available data (say 1 through n), x(i) is a per-example vector of features, and y(i) is the scalar quantity to be modeled. Only x(i) and y(i) are observed. The e(i) term is the un-modeled component of y(i) and you typically hope that the e(i) can be thought of unknowable effects, individual variation, ignorable errors, residuals, or noise. How weak/strong assumptions you put on the e(i) (and other quantities) depends on what you know, what you are trying to do, and which theorems you need to meet the pre-conditions of. The Gauss-Markov theorem assures a good estimate of B under weak assumptions.

How to interpret the theorem

The point of the Gauss-Markov theorem is that we can find conditions ensuring a good fit without requiring detailed distributional assumptions about the e(i) and without distributional assumptions about the x(i). However, if you are using Bayesian methods or generative models for predictions you may want to use additional stronger conditions (perhaps even normality of errors and even distributional assumptions on the xs).

We are going to read through the Wikipedia statement of the Gauss-Markov theorem in detail.

Continue reading Reading the Gauss-Markov theorem

Diversion: Win-Vector LLC’s Nina Zumel takes time off to publish a literary book review

Win-Vector LLC’s Nina Zumel takes some time off to publish a literary book review: Reading Red Spectres: Russian Gothic Tales.

Hundertwasser domes

Nina Zumel also examines aspects of the supernatural in literature and in folk culture at her blog, She writes about folklore, ghost stories, weird fiction, or anything else that strikes her fancy. Follow her on Twitter @multoghost.

Automatic bias correction doesn’t fix omitted variable bias

Page 94 of Gelman, Carlin, Stern, Dunson, Vehtari, Rubin “Bayesian Data Analysis” 3rd Edition (which we will call BDA3) provides a great example of what happens when common broad frequentist bias criticisms are over-applied to predictions from ordinary linear regression: the predictions appear to fall apart. BDA3 goes on to exhibit what might be considered the kind of automatic/mechanical fix responding to such criticisms would entail (producing a bias corrected predictor), and rightly shows these adjusted predictions are far worse than the original ordinary linear regression predictions. BDA3 makes a number of interesting points and is worth studying closely. We work their example in a bit more detail for emphasis. Continue reading Automatic bias correction doesn’t fix omitted variable bias

Frequentist inference only seems easy

Two of the most common methods of statistical inference are frequentism and Bayesianism (see Bayesian and Frequentist Approaches: Ask the Right Question for some good discussion). In both cases we are attempting to perform reliable inference of unknown quantities from related observations. And in both cases inference is made possible by introducing and reasoning over well-behaved distributions of values.

As a first example, consider the problem of trying to estimate the speed of light from a series of experiments.

In this situation the frequentist method quietly does some heavy philosophical lifting before you even start work. Under the frequentist interpretation since the speed of light is thought to have a single value it does not make sense to model it as having a prior distribution of possible values over any non-trivial range. To get the ability to infer, frequentist philosophy considers the act of measurement repeatable and introduces very subtle concepts such as confidence intervals. The frequentist statement that a series of experiments places the speed of light in vacuum at 300,000,000 meters a second plus or minus 1,000,000 meters a second with 95% confidence does not mean there is a 95% chance that the actual speed of light is in the interval 299,000,000 to 301,000,000 (the common incorrect recollection of what a confidence interval is). It means if the procedure that generated the interval were repeated on new data, then 95% of the time the speed of light would be in the interval produced: which may not be the interval we are looking at right now. Frequentist procedures are typically easy on the practitioner (all of the heavy philosophic work has already been done) and result in simple procedures and calculations (through years of optimization of practice).

Bayesian procedures on the other hand are philosophically much simpler, but require much more from the user (production and acceptance of priors). The Bayesian philosophy is: given a generative model, a complete prior distribution (detailed probabilities of the unknown value posited before looking at the current experimental data) of the quantity to be estimated, and observations: then inference is just a matter of calculating the complete posterior distribution of the quantity to be estimated (by correct application of Bayes’ Law). Supply a bad model or bad prior beliefs on possible values of the speed of light and you get bad results (and it is your fault, not the methodology’s fault). The Bayesian method seems to ask more, but you have to remember it is trying to supply more (complete posterior distribution, versus subjunctive confidence intervals).

In this article we are going to work a simple (but important) problem where (for once) the Bayesian calculations are in fact easier than the frequentist ones. Continue reading Frequentist inference only seems easy