Machine Learning Students Overfit to Overfitting

TL;DR

This paper examines how machine learning students often misunderstand overfitting, identifies common misconceptions and mistakes, and offers recommendations to improve teaching and understanding of this fundamental concept.

Contribution

It provides a detailed analysis of student misconceptions about overfitting and suggests targeted solutions to enhance learning and teaching effectiveness.

Findings

Students often confuse overfitting with other issues

Misconceptions hinder proper understanding of generalization

Recommendations can improve teaching strategies

Abstract

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.