mlsanityData to 14 Oct 2026

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

arXiv 2012.00058 ↗First posted 30 Nov 2020cs.LG cs.DBcited by 58

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 58 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 8; 2023: 13; 2024: 10; 2025: 11; 2026: 11.

58 papers.

Sort:

By area:

  1. 2022Exhaustive Symbolic Regressioncited by 29
  2. 2023Deep Generative Symbolic Regressioncited by 28
  3. 2022Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methodscited by 19
  4. 2025Call for Action: towards the next generation of symbolic regression benchmarkcited by 17
  5. 2022Fast Interpretable Greedy-Tree Sumscited by 13
  6. 2021Symbolic regression outperforms other models for small data setscited by 12
  7. 2024Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendationscited by 12
  8. 2022A Mixing Time Lower Bound for a Simplified Version of BARTcited by 7
  9. 2023Explanation Shift: How Did the Distribution Shift Impact the Model?cited by 6
  10. 2023Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competitioncited by 6
  11. 2024Learning a Decision Tree Algorithm with Transformerscited by 6
  12. 2024PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applicationscited by 6
38 more
  1. 2022Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Setscited by 5
  2. 2023Homological Neural Networks: A Sparse Architecture for Multivariate Complexitycited by 5
  3. 2022When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Developmentcited by 4
  4. 2024DALex: Lexicase-like Selection via Diverse Aggregationcited by 3
  5. 2024Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large pcited by 3
  6. 2021Interpretable Decision Trees Through MaxSATcited by 2
  7. 2025Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasetscited by 2
  8. 2025metabeta -- A fast neural model for Bayesian mixed-effects regressioncited by 2
  9. 2026Loss-Driven Bayesian Active Learningcited by 2
  10. 2026The Case for Model Science: Verify, Explore, Steer, Refinecited by 2
  11. 2022High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithmscited by 1
  12. 2022The BUTTER Zone: An Empirical Study of Training Dynamics in Fully Connected Neural Networkscited by 1
  13. 2023Classy Ensemble: A Novel Ensemble Algorithm for Classificationcited by 1
  14. 2023Vertical Symbolic Regressioncited by 1
  15. 2024Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applicationscited by 1
  16. 2024Concurrent vertical and horizontal federated learning with fuzzy cognitive mapscited by 1
  17. 2025Optimal Decision Tree Pruning Revisited: Algorithms and Complexitycited by 1
  18. 2025Light-Weight Benchmarks Reveal the Hidden Hardware Cost of Zero-Shot Tabular Foundation Modelscited by 1
  19. 2025Towards symbolic regression for interpretable clinical decision scorescited by 1
  20. 2026Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selectioncited by 1
  21. 2021Improve High Level Classification with a More Sensitive metric and Optimization approach for Complex Network Buildingcited by 0
  22. 2021Training Experimentally Robust and Interpretable Binarized Regression Models Using Mixed-Integer Programmingcited by 0
  23. 2021Pedagogical Rule Extraction to Learn Interpretable Models - an Empirical Studycited by 0
  24. 2023Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classificationcited by 0
  25. 2023BELLA: Black box model Explanations by Local Linear Approximationscited by 0
  26. 2023Bayesian post-hoc regularization of random forestscited by 0
  27. 2023Genetic heterogeneity analysis using genetic algorithm and network sciencecited by 0
  28. 2023An Efficient Data Analysis Method for Big Data using Multiple-Model Linear Regressioncited by 0
  29. 2023Pool-Based Active Learning with Proper Topological Regionscited by 0
  30. 2023Univariate Radial Basis Function Layers: Brain-inspired Deep Neural Layers for Low-Dimensional Inputscited by 0
  31. 2024Classification Tree-based Active Learning: A Wrapper Approachcited by 0
  32. 2024Joint Optimization of Piecewise Linear Ensemblescited by 0
  33. 2024Multi forests: Variable importance for multi-class outcomescited by 0
  34. 2025Data-Informed Model Complexity Metric for Optimizing Symbolic Regression Modelscited by 0
  35. 2025What should an AI assessor optimise for?cited by 0
  36. 2025NIMO: a Nonlinear Interpretable MOdelcited by 0
  37. 2025Probabilistic Pretraining for Neural Regressioncited by 0
  38. 2025Improving Decision Trees through the Lens of Parameterized Local Searchcited by 0

50 most cited of 58 All 58 (JSON)