Data-driven Advice for Applying Machine Learning to Bioinformatics Problems

Randal S. Olson, William La Cava, Zairah Mustahsan, Akshay Varik, Jason H. Moore
8/8/2017
q-bio.QMcs.LGstat.ML

Official code: rhiever/sklearn-benchmarks (213 stars, MIT, last commit 2017-10-25).

Abstract

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.

View on arXivView PDF

Code Implementations(1)

rhiever/sklearn-benchmarksOfficial100%
MIT

A centralized repository to report scikit-learn model performance across a variety of parameter settings and data sets.

21356Python, Jupyter NotebookFeb 3, 20168 years agoMIT
benchmarkingmachine-learningpythonscikit-learn

Similar Papers

CLASSify: A Web-Based Tool for Machine Learning

84% match2023

Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches

81% match1 repo9922019

Evaluating recommender systems for AI-driven biomedical informatics

81% match1 repo2382019

BBOmix: A Tabular Benchmark for Hyperparameter Optimization of Unsupervised Biological Representation Learning

81% match1 repo22026

Tunability: Importance of Hyperparameters of Machine Learning Algorithms

81% match1 repo92018

CANDY: A Benchmark for Continuous Approximate Nearest Neighbor Search with Dynamic Data Ingestion

81% match2024

Cite this paper

@article{olson2017datadriven,
  title  = {Data-driven Advice for Applying Machine Learning to Bioinformatics Problems},
  author = {Randal S. Olson and William La Cava and Zairah Mustahsan and Akshay Varik and Jason H. Moore},
  year   = {2017},
  eprint = {1708.05070},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1708.05070}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/1708.05070.svg)](https://codeofpaper.com/paper/1708.05070)

Discussion