Description: Data imbalance refers to a situation where the number of instances of one class is significantly greater than that of other classes, which can affect the performance of machine learning models. This phenomenon is especially relevant in the context of classification tasks, as these algorithms are highly dependent on the quality and quantity of training data. When data is imbalanced, models tend to learn patterns that favor the majority class, resulting in a model that performs poorly in classifying the minority class. This translates into misleading evaluation metrics, such as high overall accuracy but low recall or F1-score for the underrepresented classes. Data imbalance can arise in various applications, such as fraud detection, where fraudulent transactions are much less common than legitimate ones, or in medical diagnosis, where certain rare diseases may be underrepresented in datasets. Therefore, addressing data imbalance is crucial for developing machine learning models that are fair and effective in practice.
