Feature Influence

Feature influence, a crucial aspect of machine learning and data analysis, focuses on understanding how individual features contribute to model predictions and outcomes. Current research emphasizes developing robust methods for assessing feature influence, particularly in noisy real-world data, exploring techniques like advanced filtering and regularization to improve the accuracy and stability of derivative estimations in equation discovery and employing ensemble machine learning models for interpretable predictions, such as in flood forecasting. This work is significant for enhancing the reliability and explainability of AI models, leading to improved decision-making in diverse fields ranging from environmental modeling to risk assessment.