Interpretable Regression

Interpretable regression aims to build accurate predictive models while simultaneously providing transparent insights into the relationships between input features and the outcome. Current research emphasizes developing scalable algorithms, such as higher-order tensor product spline models and factorization machines, that can handle high-dimensional data and incorporate complex interactions without sacrificing interpretability. This focus on both accuracy and explainability is crucial for building trust in machine learning models across diverse scientific fields and applications, enabling better understanding of complex systems and informed decision-making. Furthermore, research is exploring methods to quantify the information leakage inherent in releasing interpretable models, balancing the need for transparency with data privacy concerns.