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ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data.

Stephen R Piccolo1, Terry J Lee1, Erica Suh1

  • 1Department of Biology, Brigham Young University, 4102 Life Sciences Building, Provo, UT, 84602, USA.

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Summary
This summary is machine-generated.

ShinyLearner integrates machine-learning packages into software containers, enabling robust benchmarking of classification algorithms. This open-source tool simplifies algorithm comparison, hyperparameter optimization, and feature selection for life science research.

Keywords:
algorithm optimizationbenchmarkclassificationfeature selectionmachine learningmodel selectionsoftware containerssupervised learning

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Machine learning classification algorithms are vital in life sciences but algorithm choice impacts accuracy.
  • Benchmarking multiple algorithms across different software packages is challenging due to compatibility issues.
  • Researchers need empirical evidence to optimize algorithm selection for specific research domains.

Purpose of the Study:

  • To develop an integrated, user-friendly platform for benchmarking classification algorithms.
  • To address challenges in comparing algorithms across diverse software packages.
  • To facilitate transparent and reproducible algorithm performance evaluation.

Main Methods:

  • Developed ShinyLearner, an open-source project using software containers to unify machine-learning packages.
  • Implemented a uniform interface for classification, irrespective of the underlying library.
  • Integrated nested cross-validation for hyperparameter optimization and feature selection.

Main Results:

  • ShinyLearner provides a standardized approach to benchmark classification and feature-selection algorithms.
  • The platform ensures transparency through tracking nested operations and generating output files.
  • A web interface simplifies the construction of benchmark comparison commands.

Conclusions:

  • ShinyLearner is a valuable resource for researchers benchmarking classification and feature-selection algorithms.
  • The project demonstrates the benefits of software containerization for user-friendly machine learning analysis.
  • Facilitates reproducible and efficient comparative analysis of machine learning models in life sciences.