Introducing XGBoost
XGBoost gets its name because it is built on the Gradient Boosting framework. Using a tree-boosting technique provides a fast method for solving ML problems. As you have seen in previous chapters, you can specify the model type, which can help speed up model training since SageMaker Autopilot does not have to determine which model type to use.
You can learn more about XGBoost here: https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html.
When you create a model with Redshift ML and specify XGBoost as the model type, and optionally specify AUTO OFF, this turns off SageMaker Autopilot and you have more control of model tuning. For example, you can specify the hyperparameters you wish to use. You will see an example of this in the Creating a binary classification model using XGBoost section.
You will have to perform preprocessing when you set AUTO to OFF. Carrying out the preprocessing ensures we will get the best possible model and is also necessary...
