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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