Overview
BOOMER is an algorithm for learning gradient boosted multi-label classification rules. It allows to train a machine learning model on labeled training data, which can afterwards be used to make predictions for unseen data. In contrast to prominent boosting algorithms like XGBoost or LightGBM, the algorithm is aimed at multi label classification problems, where individual data examples are not only associated with a single class, but may correspond to several labels at the same time.
This document is intended for users and developers that are interested in the algorithm’s implementation. For a detailed description of the used methodology, please refer to the section References.