Instructions to use Yashwanth-R19/boston-housing-rf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Yashwanth-R19/boston-housing-rf with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Yashwanth-R19/boston-housing-rf", "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] | |
| # Boston Housing Price Model | |
| Random forest regressor predicting median owner-occupied home value (`medv`, in $1000s) from the Boston Housing dataset. Trained automatically via GitHub Actions CI/CD. Data and pipeline versioned with DVC. | |
| ## Metrics | |
| - **mae**: 2.0980 | |
| - **mse**: 8.7256 | |
| - **rmse**: 2.9539 | |
| - **r2**: 0.8810 |