Feature Interaction

Feature interaction research focuses on understanding how multiple input features jointly influence model predictions, aiming to improve model accuracy, interpretability, and efficiency. Current research emphasizes developing methods to detect and quantify these interactions, often employing techniques like Shapley values, transformers, and graph-based approaches within various model architectures (e.g., CNNs, tree-based models). This work is significant because accurately modeling feature interactions is crucial for enhancing the performance and trustworthiness of machine learning models across diverse applications, from recommender systems and medical image analysis to autonomous driving and scientific discovery.