  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.
