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SPM® Webinars

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The Evolution of Regression Modeling

The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles

Webinar Title: The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles

Date/Time: Friday, March 1, 15, 29, and April 12 2013, 10am-11am, PST


Course Description:
Regression is one of the most popular modeling methods, but the classical approach has significant problems. This webinar series address these problems. Are you are 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 1

Part 1: Regression methods discussed (download slides)

  • Classical Regression
  • Logistic Regression
  • Regularized Regression: GPS Generalized Path Seeker
  • Nonlinear Regression: MARS Regression Splines

The Evolution of Regression Modeling

 
The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles
 

[J#58:1603]

Advances in TreeNet Gradient Boosting

Advances in Gradient Boosting: The Power of Post Processing

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Advances in Gradient Boosting: the Power of Post-Processing

Learn how TreeNet stochastic gradient boosting can be improved by post processing techniques such as GPS Generalized Path Seeker, RuleLearner, and ISLE.

Course Outline:

I. Gradient Boosting and Post-Processing:

  • What is missing from Gradient Boosting?
  • Why post-processing techniques are used?

II. Applications Benefiting from Post-Processing: Examples from a variety of industries.

  • Financial Services
  • Biomedical
  • Environmental
  • Manufacturing
  • Adserving

III. Typical Post-Processing Steps

 

IV. Techniques

  • Generalized Path Seeker (GPS): Modern high-speed LASSO-style regularized regression
  • Importance Sampled Learning Ensembles (ISLE): identify and reweight the most influential trees
  • RuleLearner: ISLE on “steroids.” Identify the most influential nodes and rules

V. Case Study Example

  • Output/Results without Post-Processing
  • Output/Results with Post-Processing
  • Demo

Watch the Video

Advances in Gradient Boosting

 
Advances in Gradient Boosting
 

[J#59:1603]

Combining CART and TreeNet

TreeNet Tree Ensembles and CART Decision Trees: A Winning Combination

TreeNet Tree Ensembles and CART Decision Trees: A Winning Combination

Click to View/Download PDF

 

Combining CART decision trees with TreeNet stochastic gradient boosting: A winning combination.

Learn about how you can combine the best of both tools in this 1 hour webinar.

 

Course Outline

 

I. Classification and Regression Trees Pros/Cons

II. Stochastic Gradient Boosting: a promising way to overcome the shortcomings of a single tree

III. Introducing Stochastic Gradient Boosting, a powerful modern ensemble of boosted trees

  • Methodology
  • Reporting
  • Interpretability
  • Post-Processing
  • Interaction Detection

IV. Advantages of using both Classification and Regression Trees and Tree Ensembles

 

Watch the Video

TreeNet Tree Ensembles and CART Decision Trees

 
TreeNet Tree Ensembles and CART Decision Trees: A Winning Combination
 

[J#60:1603]

[J#62:1603]

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