Re: Webinar: Improve Your Regression with Modern Regression Analysis Techniques

Posted by Bruce Weaver on Jul 22, 2016; 3:22pm
URL: http://spssx-discussion.165.s1.nabble.com/Webinar-Improve-Your-Regression-with-Modern-Regression-Analysis-Techniques-tp5732806p5732807.html

Some of these approaches (e.g., Multivariate Adaptive Regression Splines) sound like they're very susceptible to over-fitting.  A quick Google search on MARS(r) took me to this set of slides:

http://www.lans.ece.utexas.edu/courses/ee380l_ese/2013/mars.pdf

On slide 2, I find:  "MARS is a form of stepwise linear regression".

It is well-known that step-wise linear regression is great at generating over-fitted models.  E.g.,

http://www.stata.com/support/faqs/statistics/stepwise-regression-problems/

I doubt very much that adding adaptive splines to the soup will improve things.  It might even make things worse.

Lisa Solomon wrote

Improve Your Regression with Modern Regression Analysis Techniques

*                    Part 1: July 27 @ 10:00 am PDT: Linear, Nonlinear, Regularized, GPS, LARS, LASSO, Elastic Net, MARS(r)

*                    Part 2: August 10 @ 10am PDT: TreeNet(r) Gradient Boosting, RandomForests(r), ISLE(tm) and RuleLearner(r)



REGISTER NOW<http://hubs.ly/H03Lxdv0>

*        Alternative link:  http://info.salford-systems.com/improve-your-regression

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Abstract:

Join us for this two part webinar series on improving your regression using modern regression analysis techniques, presented by Senior Scientist, Mikhail Golovyna. In these webinars you will learn how to drastically improve predication accuracy in your regression with a new model that addresses common concerns such as missing values, interactions, and nonlinearities in your data.

We will demonstrate the techniques using real-world data sets and introduce the main concepts behind Leo Breiman's Random Forests and Jerome Friedman's GPS (Generalized PathSeeker(tm)), MARS(r) (Multivariate Adaptive Regression Splines), and Gradient Boosting.

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