Fig 12.opju: Machine Learning Model Performance Comparison for Load Prediction

An origin project file containing evaluation results for load prediction models. The data compares the performance of KNN, Decision Tree, XGBoost, and Random Forest models using R², RMSE, MAE, and MSE metrics. The 201.3 KB file was authored by Hongtao Zhang and last updated on June 1, 2026.

Use Cases

• Compare regression model performance based on R², RMSE, MAE, and MSE metrics.
• Benchmark KNN, Decision Tree, XGBoost, and Random Forest models for load prediction.
• Analyze the trade-offs between different error metrics for model selection.

Strengths

• File size is precisely 201.3 KB.
• Explicitly lists the four machine learning models (KNN, Decision Tree, XGBoost, Random Forest) and four evaluation metrics (R², RMSE, MAE, MSE) used for comparison.
• Has a clear license (CC-BY-4.0) and author attribution.

Limitations

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

Provenance

Source

figshare

Collection Method

Likely contains results from a machine learning experiment comparing model performance.

Freshness

Last updated 2026-06-01 17:33:02; freshness should be verified.