Interpretable Machine Learning

Explainable machine learning aims to make the decision-making processes of machine learning transparent, facilitating supervision and understanding. Its core objective is to enhance the interpretability of model predictions by developing methods that ensure the decision mechanisms are clear and visible, thereby increasing the credibility and reliability of models in practical applications.