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
metadata
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