Penn Machine Learning Benchmarks (PMLB) is a large collection of curated benchmark datasets for evaluating and comparing supervised machine learning algorithms. These datasets cover a broad range of applications including binary/multi-class classification and regression problems as well as combinations of categorical, ordinal, and continuous features.
In the interactive plotly chart below, each dot represents a dataset colored based on its associated task (classification vs. regression). In log scale, the x and y axis shows the number of observations and features respectively. Please click on the legend to hide/show the groups of datasets. Click on each dot to access the dataset’s pandas-profiling report.
Note: If a dataset has more than 20 features, we randomly
chose 20 to be displayed in its profiling report. Therefore, please
disregard the Number of variables
in the corresponding
report and, instead, use the correct n_features
in the
chart and table below.
Browse, sort, filter and search the complete table of summary statistics below.
Click on the dataset’s name to access its pandas-profiling report.
Click on the GitHub Octocat to access its metadata.
To filter, please type in the box at the bottom of each numeric
column in the format low ... high
. For example, if you want
to see all classification datasets with 80 to 100 observations,
select classification
at the bottom of Task
and type 80 ... 100
at the bottom of the
n_observations
column.
The complete table of dataset characteristics is also available for download. Please note, in our documentation, a feature is considered:
All datasets are stored in a common format:
target
\t
) separatedgzip
to conserve
spaceIf you use PMLB in a scientific publication, please consider citing one of the following papers:
Joseph D. Romano, Le, Trang T., William La Cava, John T. Gregg, Daniel J. Goldberg, Praneel Chakraborty, Natasha L. Ray, Daniel Himmelstein, Weixuan Fu, and Jason H. Moore. PMLB v1.0: an open source dataset collection for benchmarking machine learning methods. arXiv preprint arXiv:2012.00058 (2020).
@article{romano2021pmlb,
title={PMLB v1.0: an open source dataset collection for benchmarking machine learning methods},
author={Romano, Joseph D and Le, Trang T and La Cava, William and Gregg, John T and Goldberg, Daniel J and Chakraborty, Praneel and Ray, Natasha L and Himmelstein, Daniel and Fu, Weixuan and Moore, Jason H},
journal={arXiv preprint arXiv:2012.00058v2},
year={2021}
}
Olson, Randal S., William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and Jason H. Moore. PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData mining 10, no. 1 (2017): 1-13. BioData Mining 10, page 36.
BibTeX entry:
@article{Olson2017PMLB,
author="Olson, Randal S. and La Cava, William and Orzechowski, Patryk and Urbanowicz, Ryan J. and Moore, Jason H.",
title="PMLB: a large benchmark suite for machine learning evaluation and comparison",
journal="BioData Mining",
year="2017",
month="Dec",
day="11",
volume="10",
number="36",
pages="1--13",
issn="1756-0381",
doi="10.1186/s13040-017-0154-4",
url="https://doi.org/10.1186/s13040-017-0154-4"
}
PMLB was developed in the Computational Genetics Lab at the University of Pennsylvania with funding from the NIH under grant AI117694, LM010098 and LM012601. We are grateful for the support of the NIH and the University of Pennsylvania during the development of this project.