Machine Learning Students Overfit to Overfitting

Matias Valdenegro-Toro, Matthia Sabatelli
Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 207:46-51, 2023.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v207-valdenegro-toro23a, title = {Machine Learning Students Overfit to Overfitting}, author = {Valdenegro-Toro, Matias and Sabatelli, Matthia}, booktitle = {Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {46--51}, year = {2023}, editor = {Kinnaird, Katherine M. and Steinbach, Peter and Guhr, Oliver}, volume = {207}, series = {Proceedings of Machine Learning Research}, month = {19--23 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v207/valdenegro-toro23a/valdenegro-toro23a.pdf}, url = {https://proceedings.mlr.press/v207/valdenegro-toro23a.html}, 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.} }
Endnote
%0 Conference Paper %T Machine Learning Students Overfit to Overfitting %A Matias Valdenegro-Toro %A Matthia Sabatelli %B Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop %C Proceedings of Machine Learning Research %D 2023 %E Katherine M. Kinnaird %E Peter Steinbach %E Oliver Guhr %F pmlr-v207-valdenegro-toro23a %I PMLR %P 46--51 %U https://proceedings.mlr.press/v207/valdenegro-toro23a.html %V 207 %X 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.
APA
Valdenegro-Toro, M. & Sabatelli, M.. (2023). Machine Learning Students Overfit to Overfitting. Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 207:46-51 Available from https://proceedings.mlr.press/v207/valdenegro-toro23a.html.

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