  Overfitting and generalization is an important concept in Machine Learning as
only models that generalize are interesting for general applications. Yet some
students have trouble learning this important concept through lectures and
exercises. In this paper we describe common examples of students
misunderstanding overfitting, and provide recommendations for possible
solutions. We cover student misconceptions about overfitting, about solutions
to overfitting, and implementation mistakes that are commonly confused with
overfitting issues. We expect that our paper can contribute to improving
student understanding and lectures about this important topic.
