Imbalanced data problem in machine learning

Abstract

Imbalanced data is a common challenge in real-world classification tasks. This study analyzes methods and algorithms for addressing class imbalance in binary classification models. Experimental evaluations are conducted on data balancing techniques, including oversampling the minority class and undersampling the majority class. The experiments cover both tabular data and image datasets. Based on the results, the impact of these methods on model performance is assessed, and practical recommendations for effective data balancing are provided