It’s a folk theorem I sometimes hear from colleagues and clients: that you must balance the class prevalence before training a classifier. Certainly, I believe that classification tends to be easier when the classes are nearly balanced, especially when the class you are actually interested in is the rarer one. But I have always been skeptical of the claim that artificially balancing the classes (through resampling, for instance) always helps, when the model is to be run on a population with the native class prevalences.
On the other hand, there are situations where balancing the classes, or at least enriching the prevalence of the rarer class, might be necessary, if not desirable. Fraud detection, anomaly detection, or other situations where positive examples are hard to get, can fall into this case. In this situation, I’ve suspected (without proof) that SVM would perform well, since the formulation of hard-margin SVM is pretty much distribution-free. Intuitively speaking, if both classes are far away from the margin, then it shouldn’t matter whether the rare class is 10% or 49% of the population. In the soft-margin case, of course, distribution starts to matter again, but perhaps not as strongly as with other classifiers like logistic regression, which explicitly encodes the distribution of the training data.
So let’s run a small experiment to investigate this question.
Continue reading Does Balancing Classes Improve Classifier Performance?
Win-Vector LLC’s Nina Zumel and John Mount are proud to announce their new data science video course Introduction to Data Science is now available on Udemy.
Continue reading Announcing: Introduction to Data Science video course
Just a warning: double check your return types in R, especially when using different modeling packages. Continue reading Check your return types when modeling in R
While skimming Professor Hadley Wickham’s Advanced R I got to thinking about nature of the square-bracket or extract operator in R. It turns out “
[,]” is a bit more irregular than I remembered.
The subsetting section of Advanced R has a very good discussion on the subsetting and selection operators found in R. In particular it raises the important distinction of two simultaneously valuable but incompatible desiderata: simplification of results versus preservation of results. Continue reading R bracket is a bit irregular
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
We have often been asked “why is there no Kindle edition of Practical Data Science with R on Amazon.com?” The short answer is: there is an edition you can read on your Kindle: but it is from the publisher Manning (not Amazon.com). Continue reading Is there a Kindle edition of Practical Data Science with R?
As John mentioned in his last post, we have been quite interested in the recent study by Fernandez-Delgado, et.al., “Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?” (the “DWN study” for short), which evaluated 179 popular implementations of common classification algorithms over 120 or so data sets, mostly from the UCI Machine Learning Repository. For fun, we decided to do a follow-up study, using their data and several classifier implementations from
scikit-learn, the Python machine learning library. We were interested not just in classifier accuracy, but also in seeing if there is a “geometry” of classifiers: which classifiers produce predictions patterns that look similar to each other, and which classifiers produce predictions that are quite different? To examine these questions, we put together a Shiny app to interactively explore how the relative behavior of classifiers changes for different types of data sets.
Continue reading The Geometry of Classifiers
I have been working through (with some honest appreciation) a recent article comparing many classifiers on many data sets: “Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?” Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, Dinani Amorim; 15(Oct):3133−3181, 2014 (which we will call “the DWN paper” in this note). This paper applies 179 popular classifiers to around 120 data sets (mostly from the UCI Machine Learning Repository). The work looks good and interesting, but we do have one quibble with the data-prep on 8 of the 123 shared data sets. Given the paper is already out (not just in pre-print) I think it is appropriate to comment publicly. Continue reading A comment on preparing data for classifiers
In most of our data science teaching (including our book Practical Data Science with R) we emphasize the deliberately easy problem of “exchangeable prediction.” We define exchangeable prediction as: given a series of observations with two distinguished classes of variables/observations denoted “x”s (denoting control variables, independent variables, experimental variables, or predictor variables) and “y” (denoting an outcome variable, or dependent variable) then:
- Estimate an approximate functional relation
y ~ f(x).
- Apply that relation to new instances where
x is known and
y is not yet known.
An example of this would be to use measured characteristics of online shoppers to predict if they will purchase in the next month. Data more than a month old gives us a training set where both
y are known. Newer shoppers give us examples where only
x is currently known and it would presumably be of some value to estimate
y or estimate the probability of different
y values. The problem is philosophically “easy” in the sense we are not attempting inference (estimating unknown parameters that are not later exposed to us) and we are not extrapolating (making predictions about situations that are out of the range of our training data). All we are doing is essentially generalizing memorization: if somebody who shares characteristics of recent buyers shows up, predict they are likely to buy. We repeat: we are not forecasting or “predicting the future” as we are not modeling how many high-value prospects will show up, just assigning scores to the prospects that do show up.
The reliability of such a scheme rests on the concept of exchangeability. If the future individuals we are asked to score are exchangeable with those we had access to during model construction then we expect to be able to make useful predictions. How we construct the model (and how to ensure we indeed find a good one) is the core of machine learning. We can bring in any big name machine learning method (deep learning, support vector machines, random forests, decision trees, regression, nearest neighbors, conditional random fields, and so-on) but the legitimacy of the technique pretty much stands on some variation of the idea of exchangeability.
One effect antithetical to exchangeability is “concept drift.” Concept drift is when the meanings and distributions of variables or relations between variables changes over time. Concept drift is a killer: if the relations available to you during training are thought not to hold during later application then you should not expect to build a useful model. This one of the hard lessons that statistics tries so hard to quantify and teach.
We know that you should always prefer fixing your experimental design over trying a mechanical correction (which can go wrong). And there are no doubt “name brand” procedures for dealing with concept drift. However, data science and machine learning practitioners are at heart tinkerers. We ask: can we (to a limited extent) attempt to directly correct for concept drift? This article demonstrates a simple correction applied to a deliberately simple artificial example.
Image: Wikipedia: Elgin watchmaker
Continue reading Can we try to make an adjustment?
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