I am proud to announce a new Win-Vector LLC statistics video course:

Campaign Response Testing

John Mount, Win-Vector LLC

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# Tag: R

Posted on Categories Administrativia, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics## New video course: Campaign Response Testing

Campaign Response Testing

John Mount, Win-Vector LLC

Continue reading New video course: Campaign Response Testing
Posted on Categories Coding, Programming, Statistics, Tutorials## How and why to return functions in R

Posted on Categories Computer Science, Programming, Public Service Article, Tutorials13 Comments on Using closures as objects in R## Using closures as objects in R

Posted on Categories Administrativia, data science, Practical Data Science, Statistics## The Win-Vector R data science value pack

Posted on Categories data science, Expository Writing, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics20 Comments on Does Balancing Classes Improve Classifier Performance?## Does Balancing Classes Improve Classifier Performance?

Posted on Categories Administrativia, data science, Practical Data Science, Pragmatic Data Science, Statistics## Announcing: Introduction to Data Science video course

Posted on Categories Programming, Rants, Statistics4 Comments on Check your return types when modeling in R## Check your return types when modeling in R

Posted on Categories Coding, Expository Writing, Rants, Statistics5 Comments on R bracket is a bit irregular## R bracket is a bit irregular

Posted on Categories Administrativia, Practical Data Science, Pragmatic Data Science, Statistics5 Comments on Is there a Kindle edition of Practical Data Science with R?## Is there a Kindle edition of Practical Data Science with R?

Posted on Categories Coding, Computer Science, data science, Expository Writing, math programming, Pragmatic Machine Learning, Statistics4 Comments on The Geometry of Classifiers## The Geometry of Classifiers

I am proud to announce a new Win-Vector LLC statistics video course:

Campaign Response Testing

John Mount, Win-Vector LLC

Continue reading New video course: Campaign Response Testing

One of the advantages of functional languages (such as R) is the ability to create and return functions “on the fly.” We will discuss one good use of this capability and what to look out for when creating functions in R. Continue reading How and why to return functions 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

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.

Please share and Tweet! Continue reading The Win-Vector R data science value pack

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

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.