# Machine Learning Students Overfit to Overfitting

arXiv: https://arxiv.org/abs/2209.03032 · HTML: https://mlsanity.com/paper/2209.03032/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Deeper Learning By Doing: Integrating Hands-On Research Projects Into a Machine Learning Course](https://mlsanity.com/paper/2107.13671/) (2021)

## Built on by (most cited first, 1 of 1)

- [Testing for Overfitting](https://mlsanity.com/paper/2305.05792/) (2023)

## Abstract (arXiv)

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.