Machine Learning Students Overfit to Overfitting
This work tackles educational challenges in machine learning for students and instructors, but it is incremental as it builds on existing pedagogical discussions.
The paper addresses the problem of machine learning students misunderstanding overfitting, identifying common misconceptions and providing recommendations to improve teaching and learning outcomes.
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.