Instructions to use itswin01/wine-quality-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use itswin01/wine-quality-model with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("itswin01/wine-quality-model", "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
| tags: [sklearn, random-forest, regression, mlops-pipeline] | |
| # Wine Quality (Red) Prediction Model | |
| This model is trained automatically using GitHub Actions as part of an MLOps CI/CD pipeline. | |
| ## Model Details | |
| - Algorithm: Random Forest Regressor | |
| - Dataset: Wine Quality (Red) | |
| ## Evaluation Metrics | |
| - **MAE**: 0.4447 | |
| - **RMSE**: 0.5641 | |
| - **R2**: 0.5131 |