Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost

The paper presents Imbalance-XGBoost, a Python package that combines the XGBoost software with weighted and focal losses to tackle binary-imbalanced classification tasks. Though a small-scale program in terms of, the package is, to the best of the authors' knowledge, the first of its which provides an integrated implementation for the two losses on XGBoost brings a general-purpose extension on XGBoost for label-imbalanced. In this paper, the design and usage of the package are described exemplar code listings, and its convenience to be integrated into-driven Machine Learning projects is illustrated. Furthermore, as the- and second-order derivatives of the loss functions are essential for the, the algebraic derivation is discussed and it can be deemed as separate algorithmic contribution. The performances of the algorithms in the package are empirically evaluated on Parkinson's disease data set, and multiple state-of-the-art performances have been. Given the scalable nature of XGBoost, the package has great to be applied to real-life binary classification tasks, which are of large-scale and label-imbalanced.