MagicTelescope: Subsampled Telescope Particle Classification Data

A 2000-row, 100-feature subsample of the MagicTelescope dataset, created by Eddie Bergman. It contains 10 target classes and was generated using a stratified sampling method with a fixed random seed. The original data is from the OpenML platform and relates to particle detection in gamma-ray telescopes.

Use Cases

• Benchmarking multi-class classification models based on the 10 target classes.
• Feature selection experiments based on the 100 available columns.
• Testing model performance on stratified, reproducible subsamples of a larger physics dataset.

Strengths

• Stratified sampling method helps preserve class distribution in the 2000-row subset.
• Reproducible generation process with a fixed random seed (seed=0).
• Contains 100 features, providing a high-dimensional classification problem.

Limitations

• Row count is unknown for the original full dataset, limiting context for the subsample.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

OpenML (dataset ID 44125), derived by Eddie Bergman.

Collection Method

Algorithmic subsampling from the original MagicTelescope dataset.