Machine Learning Model Variable Importance Using Conventional and Novel Outcome Metrics

Variable importance results from different machine learning models, using both conventional and novel metrics as outcome variables. The dataset is a single 11.5 KB XLSX file authored by Troy J. Kieran and last updated on May 8, 2026. It is shared under a CC-BY-4.0 license on the figshare platform.

Use Cases

• Compare feature rankings across different machine learning models based on the described outcome metrics.
• Analyze the sensitivity of variable importance to the choice of conventional versus novel performance metrics.
• Benchmark feature selection algorithms using the provided model and metric results.

Strengths

• File size is precisely 11.5 KB, indicating a focused and manageable dataset.
• License is clearly specified as CC-BY-4.0, permitting broad reuse with attribution.
• Update timestamp is provided (2026-05-08 17:49:04), suggesting recent maintenance.

Limitations

• Row count is unknown, which may limit suitability assessment for large-scale analysis.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality and methodology require manual inspection.

Provenance

Source

figshare

Freshness

Last updated 2026-05-08 17:49:04; freshness should be verified.