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

arXiv: https://arxiv.org/abs/2012.00058 · HTML: https://mlsanity.com/paper/2012.00058/

## Key facts

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

## Built on by (most cited first, 30 of 58)

- [Exhaustive Symbolic Regression](https://mlsanity.com/paper/2211.11461/) (2022)
- [Deep Generative Symbolic Regression](https://mlsanity.com/paper/2401.00282/) (2023)
- [Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods](https://mlsanity.com/paper/2202.00858/) (2022)
- [Call for Action: towards the next generation of symbolic regression benchmark](https://mlsanity.com/paper/2505.03977/) (2025)
- [Fast Interpretable Greedy-Tree Sums](https://mlsanity.com/paper/2201.11931/) (2022)
- [Symbolic regression outperforms other models for small data sets](https://mlsanity.com/paper/2103.15147/) (2021)
- [Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations](https://mlsanity.com/paper/2403.08151/) (2024)
- [A Mixing Time Lower Bound for a Simplified Version of BART](https://mlsanity.com/paper/2210.09352/) (2022)
- [Explanation Shift: How Did the Distribution Shift Impact the Model?](https://mlsanity.com/paper/2303.08081/) (2023)
- [Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition](https://mlsanity.com/paper/2304.01117/) (2023)
- [Learning a Decision Tree Algorithm with Transformers](https://mlsanity.com/paper/2402.03774/) (2024)
- [PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications](https://mlsanity.com/paper/2409.01635/) (2024)
- [Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets](https://mlsanity.com/paper/2201.07724/) (2022)
- [Homological Neural Networks: A Sparse Architecture for Multivariate Complexity](https://mlsanity.com/paper/2306.15337/) (2023)
- [When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development](https://mlsanity.com/paper/2209.01083/) (2022)
- [DALex: Lexicase-like Selection via Diverse Aggregation](https://mlsanity.com/paper/2401.12424/) (2024)
- [Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large p](https://mlsanity.com/paper/2410.13681/) (2024)
- [Interpretable Decision Trees Through MaxSAT](https://mlsanity.com/paper/2110.13854/) (2021)
- [Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets](https://mlsanity.com/paper/2508.17391/) (2025)
- [metabeta -- A fast neural model for Bayesian mixed-effects regression](https://mlsanity.com/paper/2510.07473/) (2025)
- [Loss-Driven Bayesian Active Learning](https://mlsanity.com/paper/2604.11995/) (2026)
- [The Case for Model Science: Verify, Explore, Steer, Refine](https://mlsanity.com/paper/2606.01189/) (2026)
- [High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms](https://mlsanity.com/paper/2207.06028/) (2022)
- [The BUTTER Zone: An Empirical Study of Training Dynamics in Fully Connected Neural Networks](https://mlsanity.com/paper/2207.12547/) (2022)
- [Classy Ensemble: A Novel Ensemble Algorithm for Classification](https://mlsanity.com/paper/2302.10580/) (2023)
- [Vertical Symbolic Regression](https://mlsanity.com/paper/2312.11955/) (2023)
- [Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications](https://mlsanity.com/paper/2405.07662/) (2024)
- [Concurrent vertical and horizontal federated learning with fuzzy cognitive maps](https://mlsanity.com/paper/2412.12844/) (2024)
- [Optimal Decision Tree Pruning Revisited: Algorithms and Complexity](https://mlsanity.com/paper/2503.03576/) (2025)
- [Light-Weight Benchmarks Reveal the Hidden Hardware Cost of Zero-Shot Tabular Foundation Models](https://mlsanity.com/paper/2512.00888/) (2025)

## 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.