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.
Highlights
Machine learning is one of the critical bioinformatics technologies [1]
We aim to expose machine learning to practitioners– computer scientists and molecular biologists and students of biomedicine, that is, the end-users of bioinformatics’ computational approaches
We propose a hands-on approach that uses an open-source workflow-based data science toolbox that combines data visualization and machine learning
Summary
Applications of machine learning span across the entire spectra of molecular biology research, from genomics, proteomics and gene expression analysis to development of predictive models through largescale integration [2]. Overfitting is perhaps the most serious mistake one can make in machine learning In his excellent review, Simon et al [4] points out that a substantial number of the most prominent early publications on gene expression analysis overfitted the data when reporting predictive or clustering models. Simon et al [4] points out that a substantial number of the most prominent early publications on gene expression analysis overfitted the data when reporting predictive or clustering models Mistakes of these kinds are today rare, yet the problem with overfitting persists [3,5]. It is essential to convey the intricacies and facets of overfitting to students that are taught about data science, and we should include lectures on overfitting already within introductory courses of machine learning
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