Performance Comparison of Nine Machine Learning Models with 5-Fold Cross-Validation

A performance comparison of nine machine learning models, likely containing aggregated metrics from a 5-fold cross-validation process. The dataset was authored by Ling Li and last updated on April 9, 2026. It is a 9.5 KB Excel file distributed under a CC-BY-4.0 license.

Use Cases

• Benchmarking model performance based on the reported cross-validation results
• Comparing the relative effectiveness of different algorithms based on the aggregated metrics
• Teaching or demonstrating cross-validation methodology and result reporting

Strengths

• Performance metrics are reported with a mean and standard deviation, indicating statistical dispersion.
• The dataset uses a 5-fold cross-validation methodology, a standard technique for robust model evaluation.
• The dataset is small (9.5 KB), making it quick to download and inspect.

Limitations

• Row count and specific column-level documentation are absent; field semantics must be inferred after download.
• The description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Likely compiled from experimental results or simulations.

Time Range

null

Freshness

Last updated 2026-04-09 17:29:13; freshness should be verified.

Geography

null