Preprocessing algorithms play a pivotal role in data preparation, especially when it comes to enhancing the quality of training data. Among them, bias mitigation algorithms are a crucial type. These algorithms are specifically designed to be applied to training data, aiming to identify and rectify various forms of biases. By doing so, they help create a more balanced, fair, and representative dataset, which in turn can significantly improve the performance and reliability of machine learning models trained on this data.
