NYC R user/Predictive Analytics Meetup on May 12

Last night at the AOL Headquarters in NYC Max Kuhn of Pfizer presented his Caret Package. There were over 100 people in attendance. It is a great package that is often used in Predictive Analytics contests like Kaggle. A copy of his slides are are on the Meetup site here. In fact the last four talks have been great. If you are newer to these Predictive Analytics Contests start with Puniyani's slides, followed by Max's and then to then on to Alex Lin and John Myles White. This is a superb group that attracts great presenters on some really cool topics. Max will be doing a class predictive analytics in R on October 16th and 17th at the New York City Predictive Analytics World.

Stonebreaker Comments on Caffiene and Linking SciDB to R

Dr. Micheal Stonebraker of MIT had a short presentation on SciDB on Friday. SciDB is an interesting project because of its stated goal of addressing the needs of the research community. Dr. Stonebraker is one of the thought leaders when its comes to databases whether you agree with him or not. In 2008 he wrote a paper saying that Hadoop and Map Reduce were a steps backwards in terms of technology development. In 2010 Google dropped Map Reduce in favor of Caffeine. I really hope Caffeine uses Java Beans!

The meeting on Friday was to announce the release of SciDB V1.0 which is supposed to be a much more feature rich than the current V0.75. It is also interesting to note that the only analytics environment they plan to integrate with is R because that is the only one SciDB users use. While I am not sure doing a open source project that is for research only is the best idea because it may cut down on some useful contributions from the non-academic world, I do think SciDB is an interesting project.

Gelman writes about Bill James

I always knew that Professor Andrew Gelman of Columbia was a well known Statistician and Social Scientist, but when he writes an article in the Baseball Prospectus now he is famous. It is always good to see a statistician write about a sabermetrician. Although these two fields are really the same it seems they try to separate themselves from each other.

It was interesting to me that Gelmen wrote about James as a baseball outsider not too different from what James was to baseball in 1984 when he wrote the "Inside-out Perspective" article. Baseball may always need the outsider prospective to push it along because its traditions and beliefs are so deep.

I thought one interesting issue that Gelmen touched upon was how little of the real work in sabermetrics gets published. When Gelmen works on a topic he publishes a paper that discusses his approach, provides an example and the code to run the example yourself. Not so in Sabermetrics. I find little detail in the published articles and very little code. This results in people like James moving away from positions and theories without explanation. I think this hurts the development of Sabermetrics in some ways. My view on how science is developed is the path of how gravity was discovered through a series of theories that we accepted and then rejected. First there was nature abores a vacuum, then there was nature abores a vacuum up to 32 feet and then finally there was gravity at 32ft/sec.

It was a fun article to read in preparation of my attendance at the Sabermetrics Seminar at Harvard May 21-22

Heritage Health Prize goes against Open Source

Today on KDnuggets I read the Heritage Health Prize recently modified the License agreement to make the work product the sole property of Heritage Health. I think this is wrong. If you want to develop a proprietary algorithm go hire someone to do it, but to claim all the work product submitted in the competition even the ones that do not win and therefore are not paid for is just wrong.

Heritage Health can not have their cake and eat it too. Kaggle has been very clear that their site has been the develop cheaper, faster analytic tools for its customers ( the contest sponsors) at a lower cost than they could do otherwise. That is fine and the contest sponsors should use and implement the models submitted to the contest. However, what we have seen is a collaborative approach wins these competitions, and a sharing of how they did win with the larger community sometimes on the Kaggle site itself makes future models even better. If predictive analytics is going to makes the leaps forward that it really needs to do it can only happen in a open collaborative environment which not only encourages but demands the sharing of information, algorithms and approaches. If we do not, analytics will cease to progress at the rate that it has been in recent history, and we will return to the bad old days of investment companies jealously guarding their superior infinite random walks from the other investments houses.

It is no coincidence that predictive analytics took off with the advent of open source software. The R environment is a shining example of that which also wins most of the Kaggle contests. It is better than what came before and will continue to improve because of the collaborative contributions of its dedicated users.

Tristan has called for a boycott of this contest. The thread bring out some other outlandish and real issues of concern.

Things are heating up in Boston

I just came back from the Greater Boston useRs Group. In the past few months this group has really taken off. Last nights opening speaker was from frequent presenter Jeffrey Breen on using R with Databases. His topics are so relevant to such a large portion of the community that it has already been posted by R blogs  like David Smith's Revolutions Blog.

After that Mike Kane of Yale University presented the EsperR package that was written by Bryan Lewis. While the talk really delt with working on financial data streams, the application is usable in any field that does analysis of streaming data.

Another attendee of the Meeting was John Verostek. He runs the Predictive Analytics Meetup in Boston.There is a great deal of synergies between the two groups. They have an upcoming event on Text Mining Utilizing the Twitter API with R. The Greater Boston useRs Group next Meetup is June 1 with Vincent Carey on BioConductor.

In Baseball too much data is never enough

A couple of weeks ago I ran across a post for a intern position at TrackMan which uses information of ball flight to improve performance. They have been very successful in golf. In fact I tried one of their units out over the winter. This job post was more interesting to me because it was looking for an analytic intern for baseball. My first reaction was just what baseball needs more data points in a hulking cloud of data.  Bill James and the Sabermatrics guys have already culled and studied the baseball stats to death even throwing out some stats as irrelavent and creating some others that are better predictors of results.

Then I realized the error of my ways. TrackMan is looking to enrich the result data with physical data. So not just if the ball was a strike or hit or even if it was a fast ball or a curve ball, but what was its speed, location and spin at points along its trajectory from mound to plate. This is very cool. In her talk at the NYC Rusers group Amanda Cox presents a heat map of Rivera's pitches crossing the plate versus other pitchers which was a simple piece of the total pitch but explains why Rivera was better in a very clear way (22:00). I believe this has the potential to change the way pitchers pitch and batters hit.

What did I learn for R/Finance 2011

The R/finance 2011 meeting was a huge success! All the talks were just great. I do not have the time to go through each talk one by one but I do feel there were a couple of themes that ran through the entire conference. The opening speaker, Mebane Faber, and the keynote speaker,  John Bollinger, touched on two topics near to my heart. The first is that in many cases the simplified model does nearly as well as the more complex one and in some case with fewer pitfalls. The second is that models are our attempt to describe reality, but they are not reality. Therefore there is always the possibility that the model is a bad fit for the reality that it is trying to model or there exists a deviation from the  model to the reality it is describing. Both phenomenons can be exploited for advantage. Never get blindly enamored with a model and approach things with an opening mind. These ideas carried pretty consistently throughout the conference.

Parallel or High Performance Computing for R are becoming a more and more important factor in analytic computing. I am not sure if it is because to the continue growth of data in general, the enterance of HPC into general awareness through the "cloud",  or because the really cool problems seems to exist on the edge of our current capability. I believe with the exposure of more users to HPC tools for R it is time to update the various pros and cons of each approach and to benchmark them against each other with a set of set typical data set and models. I do wonder if the recent problems on Amazons EC2 could will slow down the growth of cloud computing? Lost time is one issue here but the users that lost their data could be much more reluctant to take that risk in the future.

I was also amazed at the traction that Rstudio had among this group of experienced R users. I have always held the belief that experienced users of any software package shy away for IDEs and GUIs and prefer the simple interaction of command line coding. I felt IDE were the tool for new or mid-level users. In this case, I was wrong. Rstudio appears to provide benefit to the very experienced R user to the point they are willing to change away from what they are currently doing and learn this model tool.

I thought JD Long's Dr Seuss inspired talk was the most entertaining of the confernece. It takes some talent to do that and even more to do it well. His Segue for R package is pretty cool too. Flash talks are a great format, and I wish they were used more often