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PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison
Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore
TL;DR
Machine learning lacks a central, consistently curated benchmark suite for comparing methods across diverse problems. The paper introduces and analyzes PMLB, evaluating 13 standard classifiers across 165 datasets. The datasets span varied problem types and performance levels, while the suite remains limited by its exclusion of regression, missing-value, and highly imbalanced datasets.
Problem
Machine learning benchmarking lacks a central, comprehensive, concise dataset suite, and dataset collection and curation remain difficult for researchers.
Method
The paper curates PMLB, characterizes dataset meta-features, and evaluates 13 standard classification methods across the full 165-dataset suite.
Results
PMLB datasets cluster into a handful of meta-feature categories, while biclustering reveals classes of problems and algorithms that work well or poorly together.
Takeaways & Limitations
PMLB provides an evolving benchmark resource and baseline comparisons for identifying methodological advantages and disadvantages across datasets.
Takeaways & Limitations
The current suite excludes regression and missing-value datasets and contains only a handful of highly imbalanced datasets.
Abstract
from arXiv · showhide
The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. This work is an important first step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.
1. Introduction
Machine learning benchmarking lacks a central, comprehensive, and concise dataset suite, while existing practices often rely on limited, inconsistently curated benchmarks. The paper introduces PMLB to organize benchmark datasets and support broader comparisons of method strengths and weaknesses.
- Benchmarking evaluates and compares machine learning methods on datasets treated as standards.
- Benchmarking can assess signal detection, prediction accuracy, computational complexity, and model interpretability.
- Existing benchmark datasets include real-world, simulated, and toy data, but their organization and adoption remain inconsistent.
- ML lacks a central, comprehensive, concise benchmark set that reveals established methods’ strengths and weaknesses.
- Researchers face dataset curation burdens, inconsistent formats, and evaluations based on too few datasets to reveal broader weaknesses.
- PMLB is introduced as a publicly available benchmark suite for comparing methods across a broader collection of curated datasets.
2. Penn Machine Learning Benchmark (PMLB)
PMLB consolidates benchmark datasets from multiple repositories into a standardized, accessible classification suite. Its current release contains 165 datasets characterized by meta-features and served through a Python interface.
- PMLB compiles commonly used real-world benchmark datasets from UCI, Kaggle, KEEL, and a meta-learning benchmark.
- Datasets use standardized rows and columns, numerical encodings, a shared class label, and exclude missing-data datasets.
- PMLB currently supports supervised binary and multiclass classification, with regression planned for future expansion.
- 2.1 PMLB Meta-Features: 165 datasets are characterized by instance counts, feature counts and types, endpoint type, class counts, and class imbalance.
- 2.1 PMLB Meta-Features: Most datasets have under 5,000 instances and 500 features with fairly balanced classes; roughly half are binary and half multiclass.
- An open-source Python interface fetches datasets as pandas DataFrames, with caching and preprocessing options documented in the repository.
3. Evaluating Machine Learning Methods
The study benchmarks 13 supervised classification methods across all 165 PMLB datasets using balanced accuracy. Feature scaling, grid search, and 10-fold cross-validation establish tuned performance comparisons.
- Thirteen supervised machine learning classification methods from scikit-learn were evaluated on all 165 PMLB datasets.
- The evaluated methods include Gaussian and Multinomial Naive Bayes, SGD, SVC kernels, Passive Aggressive, KNN, and Extra Trees.
- Balanced accuracy was used because it averages per-class accuracies and accounts for class imbalance.
- Features were standardized by subtracting the mean and scaling to unit variance before evaluating the methods.
- A comprehensive parameter grid search with 10-fold cross-validation selected parameters by mean cross-validation balanced accuracy.
- More than 5.5 million evaluations covered the 13 methods across the 165 datasets.
4. Results
The results characterize PMLB through dataset and model clustering, revealing relationships among meta-features, algorithms, and problem difficulty. Across the suite, performance varies substantially, but most datasets are solvable at relatively high balanced accuracy.
- Dataset clustering: PMLB datasets were clustered with k-means into five groups using normalized meta-features, projected onto PCA axes explaining 49% of variance.The cluster count balanced interpretability and separation according to the silhouette score.
- Model-dataset biclustering: Spectral biclustering grouped 13 ML methods and 165 datasets into 40 contiguous biclusters based on tuned-model balanced accuracy.The organization comprised four ML-wise clusters and ten data-wise clusters, with deviations from mean accuracy used to expose differential performance.
- Cluster structure: ML methods tended to cluster by underlying approach, while datasets from shared origins also formed clusters.Gaussian and Multinomial Naive Bayes, hyperplane estimators, and tree-based methods each formed recognizable groups; GAMETES, mfeats, Breast Cancer, Wine Quality, and thyroid datasets showed origin-related grouping.
- Performance contrasts: Datasets differed in how they exposed algorithmic strengths: noisy epistasis problems were difficult for all tested models, whereas clean2 was highly accurate for nearly every method.Parity and other interaction-heavy problems favored methods with high capacity for feature interactions over methods assuming linear independence.
- Overall performance: 87 datasets achieved balanced accuracy of at least 0.9, and 98.8% achieved at least 0.6 across the tuned models.The suite spans a reasonable difficulty range, but its datasets are biased toward problems solvable with higher balanced accuracy.
5. Discussion and Conclusion
PMLB introduces an evolving, curated benchmark suite designed to compare machine-learning methods across diverse datasets and expose methodological strengths and weaknesses. Its initial analyses reveal clustered dataset and algorithm-performance patterns, while its current scope leaves important classification domains underrepresented.
- PMLB collects and curates 165 datasets as evolving benchmark standards for comparing and evaluating machine-learning methods.The suite also provides performance baselines and assesses dataset diversity to identify shortcomings for future additions.
- Meta-feature analysis places most PMLB datasets into a handful of categories defined by feature types, class imbalance, dimensionality, and class counts.
- Biclustering algorithm performance reveals classes of problems and algorithms that work well or poorly together.
- PMLB is not yet comprehensive for supervised classification because it excludes missing-value and regression datasets and contains few highly imbalanced datasets.
- Future expansion will prioritize new datasets with varied properties and meta-features rather than multiple variants of existing datasets.Performance comparisons across diverse methods are intended to identify a limited set of standards that expose methodological advantages and disadvantages.
- A more comprehensive benchmark tool is expected to support more thorough and honest comparisons between machine-learning methods.