Random Data

Random data, encompassing both naturally occurring randomness and artificially introduced noise, is a critical consideration across diverse machine learning applications. Current research focuses on understanding how randomness impacts model performance, particularly in evaluating model robustness and generalizability, and developing methods to mitigate its negative effects, such as through data imputation techniques or adversarial attack neutralization strategies. These efforts are crucial for improving the reliability and trustworthiness of machine learning models, with implications for various fields including healthcare, social network analysis, and image classification.