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This is a tutorial/explanation of how to set up XGBoost for imbalanced classification while tuning for imbalanced data.

There are three main sections:

1. Hyperopt/Bayesian Hyperparameter Tuning
2. Focal and Crossentropy losses
3. XGBoost Parameter Meanings

(references are dropped as-needed)

Hyperopt

The hyperopt package is associated with Bergstra et. al.. The authors argued that the performance of a given model depends both on the fundamental quality of the algorithm as well as details of its tuning (also known as its hyper-parameters).