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
metadata
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