How to Deal With Imbalanced Classification and Regression Data

Data imbalance is a common challenge in machine learning that occurs when datasets have skewed distributions, particularly when the minority class is the focus of interest for tasks like fraud detection or disease diagnosis. To address this, three main strategies are employed: data-level approaches (like SMOTE for oversampling and NearMiss for undersampling), algorithm modifications (such as cost-sensitive learning and one-class classification), and hybrid approaches that combine both data and algorithm strategies. While classification with imbalanced data is well-studied, regression tasks dealing with imbalanced continuous targets remain less explored, with techniques like SMOTER and SMOGN being adaptations from classification methods. Performance metrics for imbalanced data problems include precision, recall, and ROC-AUC, which highlight model performance beyond simple accuracy. Recent advancements in handling imbalanced regression involve methods like Label Distribution Smoothing (LDS) and Feature Distribution Smoothing (FDS) to better capture the underlying data distribution. These approaches are critical for building models that can effectively learn from imbalanced datasets without biasing predictions towards the majority class.