WEBINAR: Webinar: Improve Your Regression with CART and Gradient Boosting - 16 February 2017

Improve Your Regression with CART and Gradient Boosting

Join us for our upcoming webinar:

Date: Thursday, February 16, 2017

Time: 1 pm EST, 10 am PST

Can't make it at this time? Register to receive a recorded copy of the webcast and presentation slides, which we will email out a few days after the live event.

Duration: 55 minutes

Speaker: Charles Harrison, Marketing Statistician, Salford Systems

Cost: Free 

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Abstract: In this 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.

Register here