Generalization Bounds

Generalization Bounds refer to the theoretical upper limits in machine learning that measure the difference between a model's predictive performance on unseen data and its performance on training data. The aim is to provide a mathematical framework for evaluating the model's generalization capability, ensuring that the model not only performs well on the training set but also maintains stable and reliable performance on new data. By studying and optimizing these bounds, one can guide model selection and parameter tuning, enhancing the robustness and adaptability of the model, thereby realizing greater value in practical applications.