Improve Your Regression with CART and Gradient Boosting

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.