I am working on some practical articles on variable selection, especially in the context of step-wise linear regression and logistic regression. One thing I noticed while preparing some examples is that summaries such as model quality (especially out of sample quality) and variable significances are not quite as simple as one would hope (they in fact lack a lot of the monotone structure or submodular structure that would make things easy).

That being said we have a lot of powerful and effective heuristics to discuss in upcoming articles. I am going to leave such positive results for my later articles and here concentrate on an instructive technical negative result: picking a good subset of variables is theoretically quite hard. Continue reading Variable pruning is NP hard

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.

The Big Lebowski, 1998.

Please read on for some context and my criticism.

Edit 8/25/2016: Martin Maechler generously committed a fix! Assuming this works out in testing it looks like we could see an improvement on this core function in April 2017. I really want to say “thank you” to Martin Maechler and the rest of the team for not only this, for all the things they do, and for putting up with me.

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.

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).

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.

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).

Win-Vector LLC’s Dr. Nina Zumel has a three part series on Principal Components Regression that we think is well worth your time.

Part 1: the proper preparation of data (including scaling) and use of principal components analysis (particularly for supervised learning or regression).

Part 2: the introduction of y-aware scaling to direct the principal components analysis to preserve variation correlated with the outcome we are trying to predict.

Part 3: how to pick the number of components to retain for analysis.

geom_step is an interesting geom supplied by the R package ggplot2. It is an appropriate rendering option for financial market data and we will show how and why to use it in this article.

This article is a demonstration the use of the Rvtreat variable preparation package followed by caret controlled training.

In previous writings we have gone to great lengths to document, explain and motivate vtreat. That necessarily gets long and unnecessarily feels complicated.

In this example we are going to show what building a predictive model using vtreat best practices looks like assuming you were somehow already in the habit of using vtreat for your data preparation step. We are deliberately not going to explain any steps, but just show the small number of steps we advise routinely using. This is a simple schematic, but not a guide. Of course we do not advise use without understanding (and we work hard to teach the concepts in our writing), but want what small effort is required to add vtreat to your predictive modeling practice.

One package of interest is ranger a fast parallel C++ implementation of random forest machine learning. Ranger is great package and at first glance appears to remove the “only 63 levels allowed for string/categorical variables” limit found in the Fortran randomForest package. Actually this appearance is due to the strange choice of default value respect.unordered.factors=FALSE in ranger::ranger() which we strongly advise overriding to respect.unordered.factors=TRUE in applications. Continue reading On ranger respect.unordered.factors