Repository for "Dealing with zero-inflated data: Achieving state-of-the-art with a two-fold machine learning approach"

The code associated with the paper titled "Dealing with Zero-Inflated Data: Achieving SOTA with a Two-Fold Machine Learning Approach" will be made available once the peer review is completed.

This research addresses the challenges posed by zero-inflated data through an innovative machine learning methodology, and the release of the code aims to facilitate further exploration and application of these techniques in relevant fields.

@article{rovzanec2023dealing,
  title={Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach},
  author={Ro{\v{z}}anec, Jo{\v{z}}e M and Petelin, Ga{\v{s}}per and Costa, Jo{\~a}o and Bertalani{\v{c}}, Bla{\v{z}} and Cerar, Gregor and Gu{\v{c}}ek, Marko and Papa, Gregor and Mladeni{\'c}, Dunja},
  journal={arXiv preprint arXiv:2310.08088},
  year={2023}
}