ABSTRACT
This chapter deals with ensemble learning approach, gradient boosting using XGBoost. Supervised learning uses labelled data to make predictions about unlabelled data. A basic nomenclature, notation and framework for supervised learning is laid down before cross-validation is introduced. A static representation of one of the visualizations is used to partially visualize the tree built from the food data. Boosting is a general approach to supervised learning, that generates an ensemble with M members from the training set. The learner chosen by the one-standard error method is run on the full data to determine which variables are most predictive of the beer types. Selected supervised learning techniques have been covered, along with Julia code needed for implementation.
