Advertisement
Published in:
2023 | OriginalPaper | Chapter
ImbalancedLearningRegression - A Python Package to Tackle the Imbalanced Regression Problem
Authors : Wenglei Wu, Nicholas Kunz, Paula Branco
Published in: Machine Learning and Knowledge Discovery in Databases
Publisher: Springer Nature Switzerland
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
This package helps Python users address imbalanced regression problems. Popular Python packages exist for imbalanced classification. However, there is still little Python support for imbalanced regression. Imbalanced regression is a well-known problem that occurs across domains, where a continuous target variable is poorly represented on ranges that are important to the end-user. Here, a re-sampling strategy is applied to modify the distribution of the target variable, biasing it towards the end-user interests so that downstream learning algorithms can be trained on the most relevant cases. The package provides an easy-to-use and extensible implementation of eight state-of-the-art re-sampling methods for regression, including four under-sampling and four over-sampling techniques. Code related to this paper is available at: https://github.com/paobranco/ImbalancedLearningRegression.