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Related Experiment Video

Updated: Oct 16, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods.

Joseph D Romano^1,2, Trang T Le¹, William La Cava¹

¹Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Bioinformatics (Oxford, England)

|October 22, 2021

Summary

This summary is machine-generated.

The Penn Machine Learning Benchmarks (PMLB) offers a comprehensive, user-friendly collection of benchmark datasets for evaluating machine learning and data science methods. This updated release enhances accessibility and integration into data science workflows.

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Area of Science:

Machine Learning
Data Science
Statistical Modeling

Background:

Standardized benchmark datasets are crucial for comparing novel machine learning and statistical modeling methods.
Existing tools lack a unified, user-friendly interface for accessing diverse benchmark datasets.
Integration with popular data science workflows is often limited.

Purpose of the Study:

To introduce the Penn Machine Learning Benchmarks (PMLB) v1.0, providing the largest collection of diverse, public benchmark datasets.
To offer a standardized, user-friendly interface for accessing and utilizing benchmark datasets.
To improve the evaluation of new machine learning and data science methods through enhanced accessibility and integration.

Main Methods:

Aggregation of a large number of diverse, public benchmark datasets.
Development of standardized, user-friendly interfaces for data access.
Integration with popular data science workflows and programming languages (Python and R).

Main Results:

PMLB v1.0 is the largest collection of diverse, public benchmark datasets available in one location.
Introduced critical improvements based on community feedback.
Provides Python and R interfaces for easy installation and use.

Conclusions:

PMLB facilitates standardized comparisons of machine learning and data science methods.
The v1.0 release significantly enhances the accessibility and usability of benchmark datasets.
PMLB supports reproducible research and accelerates the development of new algorithms.