Treenet and Stochastic Gradient Boosting

While I am someone who likes to go deep into techniques, I rarely get the time (maybe some intellectual honesty would require me to say that I get distracted easily by what is happening around me) to understand something technical. However there is an expectation that "I get it faster!" So for this week, I wanted to actually get deep into one such thought process and bring out concepts into simple intuitive ideas.

This week I want to get into Stochastic Gradient Boosting. Purely because I understand it well enough to explain I guess but another reason that is a little more personal is the fact that I had a dinner conversation with the inventor of this methodology. Jerome Friedman made a presentation at and event at my previous job and since I was organizing the event I was able to meet up with him for dinner along with a few other colleagues. It always feels good to be with statistical royalty and these folks are quite down to earth. While dinner was good, the conversation was better as we learned about his Princeton days when he was colleagues with John Nash the famous Nobel Prize winning economist.

Anyways coming to the key idea of this blog!!! Stochastic gradient boosting is an approach used to improve supervised learning methods. In a typical classification problem accuracy needs to be improved without overfitting the data. With any algorithm, all one can typically do is come up with better features to improve the model. There is significant learning to be had from classification error though. Wherever error is high, there is an opportunity for improvement. Modeling the error (based on any algorithm that you have already used to get this far) will allow you to further reduce it. However, there is one problem to watch out for. Errors are technically independent of the model being developed and hence we need to watch out for spurious relationships. Penalizing the error reduces the impact of these variables being able to significantly impact the analysis unless there is value coming through them being in the model. This in a nutshell is SGB and Treenet is a commercial implementation of this for decision tree algorithms.

R also has an implementation of Stochastic Gradient Boosting. Actually it has many. The GBM package is a good place to start as it has simple implementation of Bagging and one can start exploring more advanced packages that implement boosting for other algorithms including regression (l2boost) and SVM (wSVM).

I guess as a next step I should read Jerome Friedman's paper and synthesize this! 

Analytical software for analysts - are they way too complex?

Are there analytical software out there that actually make learning from data intuitive? I have experience with quite a few of these packages but none of them are intuitive for the average business analyst without making them useless after looking at data in one or two dimensions. While this is good for business, I must admit that it makes life difficult as the problems one has to tackle get quite mundane when responding to queries from the not so statistically literate. 

What would be the ideal requirements for one to actually be able to get ideas from data? Let us assume that the average user has a sense of the business he / she is dealing in. At the end of the analysis he should be able to get a sense of how to drive the business forward or at least has a good sense of what are some of the drivers that would explore further. Let us further assume that the average business user also has the ability to understand counter-intuitive results and can basically understand two dimension analysis and can possibly understand three dimension analysis but will be unable to move forward beyond that. 

Ideally when my business problems are well-defined (in the sense that I at least know what I want to solve initially even though I might realize that I need to solve something much larger later), then these tools should be able to at least drive some initial value for the analysts by incorporating these business requirements. But when I am sifting through data without a clue as to what I am looking for, how do I identify patterns that are meaningful and at the same time not require me to be in that business domain forever?

Regression analysis required significant understanding of the statistics to be able to confidently drive the analysis. CART / CHAID type algorithms are relatively easier to understand but I am not sure if there are decent implementations of a software that makes the learning from CHAID / CART intuitive. Bayesian networks or topological data analysis might be an answer but I have not worked enough with these to have a viewpoint on the implementation perspective. These are good with identifying patterns but do not necessarily make it easier for the business to get their reads better.

Ultimately I believe business problems need to be solved with the business context in mind and there are no general software that will enable that. Is it time for one to be created?