Tag Archives: R

Using closures as objects in R

For more and more clients we have been using a nice coding pattern taught to us by Garrett Grolemund in his book Hands-On Programming with R: make a function that returns a list of functions. This turns out to be a classic functional programming techique: use closures to implement objects (terminology we will explain).

It is a pattern we strongly recommend, but with one caveat: it can leak references similar to the manner described in here. Once you work out how to stomp out the reference leaks the “function that returns a list of functions” pattern is really strong.

We will discuss this programming pattern and how to use it effectively. Continue reading Using closures as objects in R

The Win-Vector R data science value pack

Win-Vector LLC is proud to announce the R data science value pack. 50% off our video course Introduction to Data Science (available at Udemy) and 30% off Practical Data Science with R (from Manning). Pick any combination of video, e-book, and/or print-book you want. Instructions below.

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Does Balancing Classes Improve Classifier Performance?

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?

R bracket is a bit irregular

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

The Geometry of Classifiers

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

A comment on preparing data for 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

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