Feature Importance Metrics for Mobility Prediction Across Six Location Categories

Aritath Siraphatwongkorn's dataset provides feature importance metrics for mobility prediction models. It contains calculated importance values across six distinct location categories using Weight, Gain, and Cover measures. The dataset was last updated in March 2026 and is shared under a CC BY 4.0 license.

Use Cases

• Compare Weight, Gain, and Cover importance metrics across the six location categories to identify dominant predictive features.
• Analyze the relative ranking of features for mobility prediction within each location category using the provided importance scores.
• Validate feature selection for location-based mobility models by benchmarking against this pre-calculated importance data.

Strengths

• Provides three distinct feature importance metrics (Weight, Gain, Cover) for comparative analysis.
• Focuses on a defined analytical scope: mobility prediction across six location categories.
• Dataset is recent, with a last update timestamp of 2026 03 26 17:48:11.

Limitations

• Extremely small scale at 9.5 KB, indicating a summary or aggregated results table, not raw modeling data.
• No raw data, rows, or columns are provided, limiting direct analytical reuse.
• The scope is narrow, focused solely on pre-computed importance scores for a specific modeling task.

Provenance

Source

Aritath Siraphatwongkorn via figshare.

Collection Method

Derived from feature importance calculations for a mobility prediction model.

Time Range

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Freshness

Last updated 2026 03 26 17:48:11.

Geography

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