Kan Shen

Kan Shen is a technique used in machine learning, particularly in analyzing data and training models. It involves breaking down complex models into smaller, manageable parts to understand how each part contributes to the overall system. This approach helps researchers identify which features or components are most influential, improve model accuracy, and troubleshoot issues. Essentially, Kan Shen enhances transparency and interpretability of machine learning models, making it easier to refine and trust their predictions, especially in applications like healthcare, finance, and other fields where understanding decision processes is critical.