R help - Mar 2013 - Hands-on Webinar: Advances in Regression: Modern Ensemble and Data Mining Approaches (no charge)

Hands-on Webinar (no charge)
Advances in Regression: Modern Ensemble and Data Mining Approaches
**Part of the series: The Evolution of Regression from Classical Linear
Regression to Modern Ensembles

Register Now for Parts 3, 4:  https://www1.gotomeeting.com/register/500959705
**All registrants will automatically receive access to recordings of Parts 1
& 2.

Course Abstract: Overcoming Linear Regression Limitations
     Regression is one of the most popular modeling methods, but the classical
approach has significant problems. This webinar series addresses these problems.
Are you working with larger datasets? Is your data challenging? Does your data
include missing values, nonlinear relationships, local patterns and
interactions? This webinar series is for you!
     In our March 29th session (Part 3), we will focus on modern ensemble and
data mining approaches. These methods dramatically improve the performance of
weak learners such as regression trees. The techniques discussed here enhance
the performance of regression trees considerably. These methods inherit the good
features of trees (variable selection, missing data, mixed predictors) and
improve on the weak features such as prediction performance.
     Did you miss parts 1 and 2? With your registration, you will receive links
to the recordings of Part 1 and 2. Covered in part 1 and 2 are  improvements to
conventional and logistic regression, as well as a discussion of classical,
regularized, and nonlinear regression from both a theoretical and hands-on point
of view. The hands-on component includes a step-by-step demonstration with
instructions for reproducing the demo at your leisure. Especially for the
dedicated student: after watching this recording, you will be able to apply
these methods to your own data.

Part 3: March 29, 10-11am PST - Regression methods discussed:

*         Nonlinear Ensemble Approaches:

o   TreeNet Gradient Boosting

o   Random Forests

o   Gradient Boosting incorporating Random Forests

*         Ensemble Post-Processing:

o   ISLE Importance Sampled Learning Ensembles

o   RuleLearner rule based learning ensembles

Part 4: April 12, 10-11am PST - Hands-on demonstration of concepts discussed in
Part 3

 *   Step-by-step demonstration
 *   Datasets and software available for download
 *   Instructions for reproducing demo at your leisure
 *   For the dedicated student: apply these methods to your own data (optional)




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