Data-driven Advice for Applying Machine Learning to Bioinformatics Problems
A very useful paper https://arxiv.org/abs/1708.05070
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.
Good to see my preferred method Random Forest close to the top of the ranking based on performance over 165 datasets.
The rankings show the strength of ensemble-based tree algorithms in generating accurate models: The first, second, and fourth-ranked algorithms belong to this class of algorithms.
All 13 ML algorithms were used as implemented in scikit-learn, a popular ML library implemented in Python.