PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
This dataset collection addresses the problem of fragmented access to benchmark datasets for machine learning researchers and practitioners, facilitating standardized comparisons of new methods.
This paper introduces PMLB v1.0, an open-source collection of diverse public benchmark datasets for evaluating machine learning and data science methods. It provides rapid access to many datasets through a standardized, user-friendly interface that integrates with popular data science workflows.
Motivation: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that provide rapid access to many of these datasets through a standardized, user-friendly interface that integrates well with popular data science workflows. Results: This release of PMLB provides the largest collection of diverse, public benchmark datasets for evaluating new machine learning and data science methods aggregated in one location. v1.0 introduces a number of critical improvements developed following discussions with the open-source community. Availability: PMLB is available at https://github.com/EpistasisLab/pmlb. Python and R interfaces for PMLB can be installed through the Python Package Index and Comprehensive R Archive Network, respectively.