Instructions to use saranka85/predictive-maintenance-random-forest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use saranka85/predictive-maintenance-random-forest with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("saranka85/predictive-maintenance-random-forest", "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
| { | |
| "model_type": "RandomForestClassifier", | |
| "source_dataset": "saranka85/predictive-maintenance-engineered-data", | |
| "target_column": "engine_condition", | |
| "feature_names": [ | |
| "engine_rpm", | |
| "lub_oil_pressure", | |
| "fuel_pressure", | |
| "coolant_pressure", | |
| "lub_oil_temp", | |
| "coolant_temp", | |
| "temperature_difference", | |
| "mean_temperature", | |
| "mean_pressure", | |
| "pressure_range", | |
| "lub_oil_pressure_per_1000_rpm", | |
| "fuel_pressure_per_1000_rpm", | |
| "rpm_fuel_pressure_interaction" | |
| ], | |
| "best_parameters": { | |
| "class_weight": null, | |
| "max_depth": 16, | |
| "max_features": "sqrt", | |
| "min_samples_leaf": 3, | |
| "n_estimators": 400 | |
| }, | |
| "best_mean_cv_f1": 0.7563717955021831, | |
| "test_metrics": { | |
| "test_accuracy": 0.6641924750447914, | |
| "test_precision": 0.6938363085213877, | |
| "test_recall": 0.8363784003248071, | |
| "test_f1": 0.7584683357879234, | |
| "test_roc_auc": 0.6889620117348952 | |
| }, | |
| "mlflow_parent_run_id": "9bc671f6b04f4fc783913cc5a0dcd898" | |
| } |