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PMLB v1.0: An open source dataset collection for benchmarking machine learning methods [article]

Joseph D. Romano, Trang T. Le, William La Cava, John T. Gregg, Daniel J. Goldberg, Natasha L. Ray, Praneel Chakraborty, Daniel Himmelstein, Weixuan Fu, Jason H. Moore
2021 arXiv   pre-print
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  ...  Motivation: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets.  ...  ACKNOWLEDGEMENTS We thank the open-source community for their valuable contributions and improvements made to PMLB during its development.  ... 
arXiv:2012.00058v3 fatcat:7n7hgofluvfu5lowpc7pgtf2w4

Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets [article]

Jun Yuan, Brian Barr, Kyle Overton, Enrico Bertini
2022
We also discuss many interesting observations that can be useful for future research on designing effective rule-based VA systems.  ...  First, we present Hierarchical Surrogate Rules (HSR), an algorithm that generates hierarchical rules based on user-defined parameters.  ...  Lee, “A unified approach to interpreting “Pmlb v1.0: an open source dataset collection for benchmarking model predictions,” in Advances in neural information processing machine  ... 
doi:10.48550/arxiv.2201.07724 fatcat:kr5duuxjynea7fbryhspsnx5q4