Building Gradient Boosting Models: Model Building Process and Principles

This is the third video in a series on building Gradient Boosting Models. We continue working through the Ames housing dataset, starting by building a Random Forest as a baseling model. We then move to XGBoost and discuss some of its most prominent improvements over the previous gradient boositing approaches. Next we recommend an approach using early stopping with a low learning_rate to set the three main hyperparameters: max_depth, learning_rate, and n_estimators. Github: https://github.com/numeristical/resou...