In SPM v7.0 (Salford Systems' latest product release) one of the new features available in the TreeNet component (Ultra version) is the ability to build Random Forests models. By building the model in the TreeNet engine, you're able to take advantage of the powerful technology offered by gradient-boosted trees.
Salford Systems recently attended the Predictive Analytics World (PAW) conference in San Francisco as a sponsor. Manning the exhibit booth was yours truly, and I was fortunate to meet many analysts, predictive modelers, and data scientists of all experience levels. Even though this is always an entertaining break from every-day office life, my favorite part of the conference was being able to participate in a workshop offered by Dean Abbott, President of Abbott Analytics, entitled "Supercharging Prediction: Hands-On With Ensembles Models."
During the course of Salford Systems' 4-part webinar series "The Evolution of Regression," some very good questions from the audience have made their way to presenter Dr. Dan Steinberg, CEO and Founder. Here are a few responses that we thought would benefit everyone who is interested in regression, nonlinear regression, regularized regression, decision tree ensembles and post-processing techniques.
This week's blog article features a great entry-level tutorial video on how to build predictive models with TreeNet stochastic gradient boosting. Check it out, enjoy, and share your comments!
In a recent webinar series, Salford Systems introduced the newest model compression and post-processing techniques available in SPM v7.0; GPS Generalized Path Seeker, ISLE and RuleLearner.
If you're an analyst or statistican working on a limited budget, this is a must-read!
What is TreeNet stochastic gradient boosting?
TreeNet is a modern ensemble approach to machine learning. Dr. Jerome Friedman at Stanford University developed its function approximation. Friedman is also a co-creator of CART Classification and Regression Trees and the author of MARS Multivariate Adaptive Regression Splines, PRIM, Projection Pursuit, etc.
While TreeNet (Stochastic Gradient Boosting) can work phenomenally well out of the box it almost always pays to try to tune your control parameters. Devoting time to optimizing a TreeNet model can improve its out of sample performance noticeably.
How Large A Sample Do I Need? Or, Can I Achieve first class results with just a few hundred training samples?
This use case from one of Salford Systems' clients in Sao Paolo, Brazil was presented at the 2012 Salford Analytics and Data Mining Conference earlier this year. A short clip from the presentation is viewable here: