Distributional Generalization

Distributional Generalization is a concept in machine learning that focuses on the distribution of errors made by a classifier, rather than just the average error. It is important to consider this type of generalization because it better captures the range of errors that can occur over an input domain. Understanding Distributional Generalization When a classifier is trained on a set of data, it learns to produce an output based on the inputs it receives. However, this output is rarely perfect