Subscribe to DSC Newsletter

Webinar: Improve Your Regression with CART and Gradient Boosting

(live 2/16, & on-demand)

In this 55-minute webinar we'll introduce you to a powerful tree-based machine learning algorithm called gradient boosting. Gradient boosting often outperforms linear regression, Random Forests, and CART. Boosted trees automatically handle variable selection, variable interactions, nonlinear relationships, outliers, and missing values.

We'll see that CART decision trees are the foundation of gradient boosting and discuss some of the advantages of boosting versus a Random Forest. We will explore the gradient boosting algorithm and discuss the most important modeling parameters like the learning rate, number of terminal nodes, number of trees, loss functions, and more. We will demonstrate using an implementation of gradient boosting (TreeNet® Software) to fit the model and compare the performance to a linear regression model, a CART tree, and a Random Forest.

Speaker: Charles Harrison, Marketing Statistician -- Salford Systems 

Date: Live Thursday, February 16, 2017 and On-Demand

Reserve your Webinar seat now 

After registering you will receive a confirmation email containing information about joining the webinar.

© Salford Systems 
9685 Via Excelencia, Suite 208 
San Diego, CA 92126 

Views: 284


  • Add Videos
  • View All

© 2020   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service