Tomorrow, Friday March 15
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
>
> 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-systems.com/videos/tutorials/805-the-evolution-of-regression-modeling-part-1
>
> 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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