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Open Data Bay Area Message Board › Hands-on Webinar Series (no charge) The Evolution of Regression from Classic

Hands-on Webinar Series (no charge) The Evolution of Regression from Classical Linear Regression to Modern Ensembles

Lisa S.
lisasolomon
San Diego, CA
Post #: 133
Maybe you missed Part 1 of "The Evolution of Regression Modeling from Classical Linear Regression to Modern Ensembles" webinar series, but you can still join for Parts 2, 3, & 4

Register Now for Parts 2, 3, 4: https://www1.gotomeeting.com/register/500959705­

Download (optional) a free evaluation of the SPM software suite v7.0 (used in the hands-on components of the webinar). As a webinar participant you will qualify for a 60-Day Evaluation of the software at no charge: http://2.salford-systems.com/the-salford-predictive-modeler-download/­

Course Outline: 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! We will cover improvements to conventional and logistic regression, and will include a discussion of classical, regularized, and nonlinear regression, as well as modern ensemble and data mining approaches. This series will be of value to any classically trained statistician or modeler.

Part 2 (Hands-on): March 15, 10-11am PST - Hands-on demonstration of concepts discussed in Part 1 (Classical Regression, Logistic Regression, Regularized Regression: GPS Generalized Path Seeker, Nonlinear Regression: MARS Regression Splines)
• 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)
• Part 1 recording: http://www.salford-sy...­

Part 3: March 29, 10-11am PST - Regression methods discussed
*Part 1 is a recommended pre-requisite
• Nonlinear Ensemble Approaches: TreeNet Gradient Boosting; Random Forests; Gradient Boosting incorporating RF
• Ensemble Post-Processing: ISLE; RuleLearner

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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