Data Preprocessing

Data preprocessing, the crucial initial step in machine learning pipelines, aims to transform raw data into a format suitable for model training and analysis. Current research emphasizes efficient preprocessing techniques, particularly for large datasets, exploring hardware acceleration and novel algorithms to overcome computational bottlenecks and improve model explainability, especially in sensitive domains like medicine. These advancements are vital for enhancing the accuracy, efficiency, and fairness of machine learning models across diverse applications, from medical diagnosis to cybersecurity and climate modeling.