Input Feature

Input features are the raw data fed into machine learning models, and their effective selection and representation are crucial for model performance and interpretability. Current research focuses on optimizing feature selection methods, including those leveraging large language models and spectral analysis, as well as developing techniques for efficient handling of high-dimensional data, such as feature slicing and structured pruning. These advancements are significant because they improve model accuracy, reduce computational costs, enhance interpretability, and address privacy concerns by enabling data minimization at inference time.