Within statistics, oversampling and undersampling in dataanalysis are techniques used to adjust the class distribution of a data set (i.e. the ratio between the different classes/categories represented). Nov 21, 2024 · Oversampling can boost model performance in imbalanced datasets but runs the risk of overfitting, while non-oversampling methods like undersampling or class weighting can help avoid... Mar 23, 2025 · In this research, we aim to present a comprehensive review of recent resampling approaches developed to handle class imbalance through a data-centric lens. Feb 2, 2026 · Imbalanced data occurs when one class has far more samples than others, causing models to favour the majority class and perform poorly on the minority class. This often results in misleading accuracy, especially in critical applications like fraud detection or medical diagnosis. Apr 29, 2021 · Undersampling should mostly not be preferred because it causes a huge amount of data loss. In the end, we are giving so much effort to collect data and it basically does not make sense when we throw them away. The issue is here is that you lose samples where your model could learn new things. Oct 13, 2020 · This data is based on crops harvested by various farmers at the end of harvest season. To simplify the problem, you can assume that all other factors like variations in farming techniques have been controlled for.
