Description: An imbalanced dataset refers to a situation where different classes within a dataset are not represented equally. This means that some classes have a significantly higher number of examples compared to others. This imbalance can negatively affect the performance of supervised learning models, as algorithms tend to favor the more represented classes, leading to low accuracy in predicting minority classes. The main characteristics of an imbalanced dataset include the unequal distribution of classes, which can result in bias in the model’s decision-making. The relevance of addressing this issue lies in the need to ensure that artificial intelligence and machine learning models are fair and accurate, especially in critical applications such as fraud detection, medical diagnosis, and image classification. To mitigate the impact of imbalance, data preprocessing techniques can be employed, such as oversampling the minority class, undersampling the majority class, or generating synthetic data. In the context of machine learning, imbalance can be particularly problematic, as many algorithms require large amounts of data to generalize properly.
