| library_name: scikit-learn | |
| tags: | |
| - regression | |
| - tabular-regression | |
| - fuel-consumption | |
| - fuelcast | |
| # FuelCast Regression Model | |
| This model predicts `Consumer_Total_MomentaryFuel` from ship, propulsion, and | |
| weather variables from the FuelCast dataset. | |
| ## Training | |
| - Dataset: `krohnedigital/FuelCast` | |
| - Config: `cps_poseidon` | |
| - Model: `random_forest` | |
| - Rows used: `30000` | |
| - Target: `Consumer_Total_MomentaryFuel` | |
| ## Metrics | |
| | Metric | Value | | |
| | --- | ---: | | |
| | R2 | 0.9957 | | |
| | RMSE | 0.0295 | | |
| | MAE | 0.0194 | | |
| ## Files | |
| - `fuelcast_model.joblib`: complete scikit-learn pipeline | |
| - `metrics.json`: evaluation metrics | |
| - `input_schema.json`: expected raw input columns | |
| - `sample_input.json`: example input row | |
| - `feature_importances.csv`: feature importance when available | |
| ## Usage | |
| ```python | |
| from huggingface_hub import hf_hub_download | |
| import joblib | |
| import pandas as pd | |
| model_path = hf_hub_download( | |
| repo_id="username/fuelcast-model", | |
| filename="fuelcast_model.joblib", | |
| ) | |
| model = joblib.load(model_path) | |
| sample = pd.DataFrame([{"Ship_SpeedOverGround": 12.4}]) | |
| prediction = model.predict(sample) | |
| ``` | |