Randal S. Olson†, William La Cava†, Zairah Mustahsan, Akshay Varik, Jason H. Moore
Institute for Biomedical Informatics, University of Pennsylvania
†Authors contributed equally to this work
Email: rso@randalolson.com, lacava@upenn.edu, jhmoore@upenn.edu
Pacific Symposium on Biocomputing 23:192-203(2018)
© 2018 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems.