Real Datasets

Real datasets are crucial for training and evaluating machine learning models, but their limitations—such as small sample sizes, label delays, biases, and high dimensionality—drive much current research. Efforts focus on developing methods to mitigate these issues, including synthetic data generation to augment real data, advanced feature selection techniques for high-dimensional data, and innovative approaches to combine observational and randomized data for improved model accuracy and robustness. These advancements are vital for improving the reliability and generalizability of machine learning across diverse applications, from fraud detection to medical diagnosis.