Analcatdata Supreme: U.S. Supreme Court Cases for Regression Benchmark

A tabular dataset from the 'Analyzing Categorical Data' book collection, used in a machine learning benchmark for regression on categorical and numerical features. The original source is unknown, but the data is associated with U.S. Supreme Court cases. It was prepared for the LeoGrin/tabular-benchmark repository.

Use Cases

• Benchmarking regression model performance based on the dataset's categorical and numerical feature mix.
• Analyzing patterns in legal outcomes based on case characteristics mentioned in the description.
• Studying feature engineering and preprocessing techniques for categorical data in a legal domain context.

Strengths

• Dataset is part of a published academic collection from the book 'Analyzing Categorical Data'.
• Data has been prepared for a specific, cited tabular data benchmark (LeoGrin/tabular-benchmark), ensuring a standard transformation.
• License is explicitly stated as us-pd, permitting free use for scientific and educational purposes with citation.

Limitations

• Column-level documentation and sample data are unavailable, requiring manual inspection after download.
• Original source, time range, row count, and file formats are unknown, limiting suitability assessment.
• Last update date is unknown; freshness is unverified.

Provenance

Source

Collection from the book 'Analyzing Categorical Data' by Jeffrey S. Simonoff.

Collection Method

Transformed for use in the tabular-benchmark GitHub repository; original gathering method is unknown.

Time Range

Temporal coverage is unknown.

Freshness

Last updated date is unknown.

Geography

Likely United States, given the Supreme Court association, but not explicitly confirmed.