Publication Date
3-1-2021
Journal
PLOS Computational Biology
DOI
10.1371/journal.pcbi.1008671
PMID
33661899
PMCID
PMC7932115
PubMedCentral® Posted Date
3-4-2021
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Computational Biology, Data Science, Humans, Machine Learning, Models, Biological, Models, Statistical, Software
Abstract
Overfitting is one of the critical problems in developing models by machine learning. With machine learning becoming an essential technology in computational biology, we must include training about overfitting in all courses that introduce this technology to students and practitioners. We here propose a hands-on training for overfitting that is suitable for introductory level courses and can be carried out on its own or embedded within any data science course. We use workflow-based design of machine learning pipelines, experimentation-based teaching, and hands-on approach that focuses on concepts rather than underlying mathematics. We here detail the data analysis workflows we use in training and motivate them from the viewpoint of teaching goals. Our proposed approach relies on Orange, an open-source data science toolbox that combines data visualization and machine learning, and that is tailored for education in machine learning and explorative data analysis.