Model Performance Benchmark for Majority-Class, XGBoost, and Fine-Tuned Models

Benchmark performance metrics for multiple models on a common test subset. The dataset, 5.5 KB in size, was authored by Eui Jun Lee and last updated on June 4, 2026. It is available under a CC-BY-4.0 license on the figshare platform.

Use Cases

• Compare model performance against a majority-class baseline based on the described benchmark.
• Evaluate the effectiveness of fine-tuning a pre-trained model based on the described test subset.
• Assess the performance of an XGBoost model on structured data relative to other approaches mentioned in the description.

Strengths

• Provides a direct comparison of multiple modeling approaches, as stated in the title.
• Small file size (5.5 KB) allows for quick download and inspection.
• Clear license (CC-BY-4.0) permits open reuse and modification.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Eui Jun Lee via figshare

Freshness

Last updated 2026-06-04 17:25:19; freshness should be verified.