Variable Importance

Variable importance quantifies the influence of individual input variables on a model's predictions, aiming to enhance model interpretability and facilitate feature selection. Current research focuses on addressing challenges like high dimensionality, correlated features, and class imbalance, employing methods such as conditional permutation importance (CPI), Shapley values, and novel adaptations of random forests and neural networks. These advancements improve the reliability and accuracy of variable importance assessments, impacting fields like medicine, social sciences, and engineering by enabling more trustworthy and insightful analyses of complex datasets.