Glass Box

"Glass-box" models in machine learning prioritize transparency and interpretability, aiming to provide insights into how a model arrives at its predictions, unlike opaque "black-box" models. Current research focuses on applying glass-box approaches—including methods like logistic regression, k-Nearest Neighbors, decision trees, and explainable boosting machines—across diverse fields such as natural language processing, wind power forecasting, and structural engineering. This emphasis on explainability enhances trust and allows for validation against existing scientific knowledge, improving both the reliability of predictions and the understanding of complex systems. The resulting models offer a valuable balance between high accuracy and the crucial ability to understand the reasoning behind predictions.