Interpretable Hybrid

Interpretable hybrid models combine the strengths of traditional, explainable models with the predictive power of complex machine learning algorithms, aiming to improve both accuracy and interpretability in diverse scientific domains. Current research focuses on developing hybrid architectures, such as mixtures of experts and combinations of neural networks with rule-based systems, and employing techniques like partial information decomposition to enhance feature selection and model understanding. This approach is proving valuable across various fields, from predicting bird migration patterns and supply chain backorders to analyzing urban health disparities and customer behavior, offering more reliable predictions while providing crucial insights into underlying causal relationships. The resulting improved interpretability facilitates better decision-making and knowledge discovery.