PMLB: a large benchmark suite for machine learning evaluation and comparison.

Area of Science:

• Computer Science
• Data Mining
• Machine Learning

Background:

• Selecting and developing machine learning (ML) methods is complex, influenced by study-specific goals.
• Inconsistent organization of benchmark datasets creates a burden for ML practitioners and data scientists.
• A need exists for standardized, accessible benchmark resources.

Purpose of the Study:

• To introduce a public, curated benchmark resource for evaluating ML methodologies.
• To characterize the diversity of available datasets using meta-feature comparison.
• To analyze the performance clustering of ML algorithms across benchmark datasets.

Main Methods:

• Development of an accessible, curated public benchmark resource.
• Meta-feature comparison of datasets within the resource to assess data diversity.
• Application of established ML methods to the benchmark suite for performance analysis.

Main Results:

• The study identified a lack of diversity in existing ML benchmarks.
• Analysis revealed gaps in current benchmarking problems.
• Performance clustering indicated limitations in the ability of current benchmarks to differentiate ML algorithms effectively.

Conclusions:

• The developed resource aids in understanding ML methodology limitations.
• This work is a step towards more diverse and efficient future benchmarking standards.
• It highlights the need for improved and expanded benchmark suites for robust ML evaluation.