Instructions to use muthuk1/fairrelay-delivery-time with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use muthuk1/fairrelay-delivery-time with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("muthuk1/fairrelay-delivery-time", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
File size: 981 Bytes
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"medae_minutes": 46.0323974609375,
"rmse_minutes": 105.68263941277179,
"r2": 0.6205830628423951,
"mape_pct": 33.39416553475195,
"cv_r2_mean": 0.5580012691222771,
"cv_r2_std": 0.2773272658331809,
"train_time_s": 0.8810091018676758,
"features": [
"num_packages",
"num_stops",
"total_distance_km",
"avg_distance_km",
"spatial_spread_km",
"start_hour",
"active_hours",
"packages_per_stop"
],
"feature_importances": {
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"num_stops": 0.23067323863506317,
"total_distance_km": 0.1215955913066864,
"avg_distance_km": 0.07093983888626099,
"spatial_spread_km": 0.1569100320339203,
"start_hour": 0.0712938979268074,
"active_hours": 0.08522961288690567,
"packages_per_stop": 0.10549156367778778
},
"data_sources": {
"lade_d_shanghai_hangzhou": 12906,
"synthetic": 20000
},
"train_samples": 26324,
"test_samples": 6582
} |