Description: The generation of imbalanced data refers to the process of creating synthetic data aimed at addressing the issue of class imbalance in datasets. This phenomenon occurs when one class of data is overrepresented compared to others, which can lead machine learning models to be trained in a biased manner, favoring the majority class and thus reducing their ability to generalize and accurately predict minority classes. The generation of imbalanced data seeks to balance these classes by creating additional examples for the underrepresented classes, using various generative model techniques. These models may include algorithms such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), which are capable of learning the distribution of existing data and generating new examples that resemble the original data. This approach not only improves model accuracy but also allows for better representation of minority classes, which is crucial in applications where these classes are of great importance, such as in fraud detection or medical diagnostics. In summary, the generation of imbalanced data is an essential technique in the field of machine learning, aimed at mitigating the impact of class imbalance and improving the effectiveness of predictive models.
