vtreat 0.5.27 released on CRAN

Posted on Categories Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, StatisticsTags , Leave a comment on vtreat 0.5.27 released on CRAN

Win-Vector LLC, Nina Zumel and I are pleased to announce that ‘vtreat’ version 0.5.27 has been released on CRAN.

vtreat is a data.frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner.

(from the package documentation)

Very roughly vtreat accepts an arbitrary “from the wild” data frame (with different column types, NAs, NaNs and so forth) and returns a transformation that reliably and repeatably converts similar data frames to numeric (matrix-like) frames (all independent variables numeric free of NA, NaNs, infinities, and so on) ready for predictive modeling. This is a systematic way to work with high-cardinality character and factor variables (which are incompatible with some machine learning implementations such as random forest, and also bring in a danger of statistical over-fitting) and leaves the analyst more time to incorporate domain specific data preparation (as vtreat tries to handle as much of the common stuff as practical). For more of an overall description please see here.

We suggest any users please update (and you will want to re-run any “design” steps instead of mixing “design” and “prepare” from two different versions of vtreat).

For what is new in version 0.5.27 please read on. Continue reading vtreat 0.5.27 released on CRAN

My criticism of R numeric summary

Posted on Categories Rants, Statistics, TutorialsTags , 8 Comments on My criticism of R numeric summary

My criticism of R‘s numeric summary() method is: it is unfaithful to numeric arguments (due to bad default behavior) and frankly it should be considered unreliable. It is likely the way it is for historic and compatibility reasons, but in my opinion it does not currently represent a desirable set of tradeoffs. summary() likely represents good work by high-ability researchers, and the sharp edges are due to historically necessary trade-offs.


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The Big Lebowski, 1998.

Please read on for some context and my criticism. Continue reading My criticism of R numeric summary

The Win-Vector parallel computing in R series

Posted on Categories Administrativia, Programming, Statistics, TutorialsTags , Leave a comment on The Win-Vector parallel computing in R series

With our recent publication of “Can you nest parallel operations in R?” we now have a nice series of “how to speed up statistical computations in R” that moves from application, to larger/cloud application, and then to details.

For your convenience here they are in order:

  1. A gentle introduction to parallel computing in R
  2. Running R jobs quickly on many machines
  3. Can you nest parallel operations in R?

Please check it out, and please do Tweet/share these tutorials.

Can you nest parallel operations in R?

Posted on Categories Programming, TutorialsTags , , , 2 Comments on Can you nest parallel operations in R?

Parallel programming is a technique to decrease how long a task takes by performing more parts of it at the same time (using additional resources). When we teach parallel programming in R we start with the basic use of parallel (please see here for example). This is, in our opinion, a necessary step before getting into clever notation and wrapping such as doParallel and foreach. Only then do the students have a sufficiently explicit interface to frame important questions about the semantics of parallel computing. Beginners really need a solid mental model of what services are really being provided by their tools and to test edge cases early.

One question that comes up over and over again is “can you nest parLapply?”

The answer is “no.” This is in fact an advanced topic, but it is one of the things that pops up when you start worrying about parallel programming. Please read on for what that is the right answer and how to work around that (simulate a “yes”).

I don’t think the above question is usually given sufficient consideration (nesting parallel operations can in fact make a lot of sense). You can’t directly nest parLapply, but that is a different issue than can one invent a work-around. For example: a “yes” answer (really meaning there are work-arounds) can be found here. Again this is a different question than “is there a way to nest foreach loops” (which is possible through the nesting operator %.% which presumably handles working around nesting issues in parLapply).

Continue reading Can you nest parallel operations in R?

The magrittr monad

Posted on Categories Computer ScienceTags , 1 Comment on The magrittr monad

Monads are a formal theory of composition where programmers get to invoke some very abstract mathematics (category theory) to argue the minutia of annotating, scheduling, sequencing operations, and side effects. On the positive side the monad axioms are a guarantee that related ways of writing code are in fact substitutable and equivalent; so you want your supplied libraries to obey such axioms to make your life easy. On the negative side, the theory is complicated.

In this article we will consider the latest entry of our mad “programming theory in R series” (see Some programming language theory in R, You don’t need to understand pointers to program using R, Using closures as objects in R, and How and why to return functions in R): category theory!

Continue reading The magrittr monad

On accuracy

Posted on Categories Administrativia, Statistics, Statistics To English Translation, TutorialsTags , Leave a comment on On accuracy

In our last article on the algebra of classifier measures we encouraged readers to work through Nina Zumel’s original “Statistics to English Translation” series. This series has become slightly harder to find as we have use the original category designation “statistics to English translation” for additional work.

To make things easier here are links to the original three articles which work through scores, significance, and includes a glossery.

A lot of what Nina is presenting can be summed up in the diagram below (also by her). If in the diagram the first row is truth (say red disks are infected) which classifier is the better initial screen for infection? Should you prefer the model 1 80% accurate row or the model 2 70% accurate row? This example helps break dependence on “accuracy as the only true measure” and promote discussion of additional measures.


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A budget of classifier evaluation measures

Posted on Categories Mathematics, StatisticsTags , , , , , , 2 Comments on A budget of classifier evaluation measures

Beginning analysts and data scientists often ask: “how does one remember and master the seemingly endless number of classifier metrics?”

My concrete advice is:

  • Read Nina Zumel’s excellent series on scoring classifiers.
  • Keep notes.
  • Settle on one or two metrics as you move project to project. We prefer “AUC” early in a project (when you want a flexible score) and “deviance” late in a project (when you want a strict score).
  • When working on practical problems work with your business partners to find out which of precision/recall, or sensitivity/specificity most match their business needs. If you have time show them and explain the ROC plot and invite them to price and pick points along the ROC curve that most fit their business goals. Finance partners will rapidly recognize the ROC curve as “the efficient frontier” of classifier performance and be very comfortable working with this summary.

That being said it always seems like there is a bit of gamesmanship in that somebody always brings up yet another score, often apparently in the hope you may not have heard of it. Some choice of measure is signaling your pedigree (precision/recall implies a data mining background, sensitivity/specificity a medical science background) and hoping to befuddle others.


Mathmanship

Stanley Wyatt illustration from “Mathmanship” Nicholas Vanserg, 1958, collected in A Stress Analysis of a Strapless Evening Gown, Robert A. Baker, Prentice-Hall, 1963

The rest of this note is some help in dealing with this menagerie of common competing classifier evaluation scores.

Continue reading A budget of classifier evaluation measures

vtreat version 0.5.26 released on CRAN

Posted on Categories Administrativia, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, StatisticsTags , , , , 1 Comment on vtreat version 0.5.26 released on CRAN

Win-Vector LLC, Nina Zumel and I are pleased to announce that ‘vtreat’ version 0.5.26 has been released on CRAN.

‘vtreat’ is a data.frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner.

(from the package documentation)

‘vtreat’ is an R package that incorporates a number of transforms and simulated out of sample (cross-frame simulation) procedures that can:

  • Decrease the amount of hand-work needed to prepare data for predictive modeling.
  • Improve actual model performance on new out of sample or application data.
  • Lower your procedure documentation burden (through ready vtreat documentation and tutorials).
  • Increase model reliability (by re-coding unexpected situations).
  • Increase model expressiveness (by allowing use of more variable types, especially large cardinality categorical variables).

‘vtreat’ can be used to prepare data for either regression or classification.

Please read on for what ‘vtreat’ does and what is new. Continue reading vtreat version 0.5.26 released on CRAN

y-aware scaling in context

Posted on Categories Exciting Techniques, Practical Data Science, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsTags , , , Leave a comment on y-aware scaling in context

Nina Zumel introduced y-aware scaling in her recent article Principal Components Regression, Pt. 2: Y-Aware Methods. I really encourage you to read the article and add the technique to your repertoire. The method combines well with other methods and can drive better predictive modeling results.

From feedback I am not sure everybody noticed that in addition to being easy and effective, the method is actually novel (we haven’t yet found an academic reference to it or seen it already in use after visiting numerous clients). Likely it has been applied before (as it is a simple method), but it is not currently considered a standard method (something we would like to change).

In this note I’ll discuss some of the context of y-aware scaling. Continue reading y-aware scaling in context

Another note on differential privacy

Posted on Categories OpinionTags , , , Leave a comment on Another note on differential privacy

I want to recommend an excellent article on the recent claimed use of differential privacy to actually preserve user privacy: “A Few Thoughts on Cryptographic Engineering” by Matthew Green.

After reading the article we have a few follow-up thoughts on the topic. Continue reading Another note on differential privacy