| { | |
| "model_name": "CBC Retail Demand Forecaster", | |
| "hf_repo": "careerbytecode/mlops-ref-retail-demand", | |
| "task": "regression (next-hour demand forecast, hourly time series)", | |
| "model_type": "XGBoost regressor, 12 past-only lag/rolling/calendar features", | |
| "framework": "xgboost", | |
| "serialization": "joblib (full XGBRegressor)", | |
| "loader": "joblib.load -> XGBRegressor; call .predict(DataFrame[FEATURES]) -> predicted trips", | |
| "random_state": 42, | |
| "feature_columns": [ | |
| "lag_1", | |
| "lag_2", | |
| "lag_3", | |
| "lag_24", | |
| "lag_168", | |
| "roll_mean_24", | |
| "roll_mean_168", | |
| "roll_std_24", | |
| "hour", | |
| "day_of_week", | |
| "is_weekend", | |
| "day_of_month" | |
| ], | |
| "split": { | |
| "train": 460, | |
| "test": 116, | |
| "method": "forward time-ordered 80/20" | |
| }, | |
| "dataset": "NYC Yellow Taxi Jan-2024 hourly (744h), NYC.gov Terms of Use", | |
| "python_version": "3.14.4", | |
| "library_versions": { | |
| "xgboost": "3.2.0", | |
| "scikit-learn": "1.8.0", | |
| "pandas": "2.3.3", | |
| "numpy": "2.4.6", | |
| "joblib": "1.5.3" | |
| }, | |
| "training_date": "2026-06-04T20:25:14.625353+00:00" | |
| } |