Instructions to use Radhakris55/boston-random-forest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Radhakris55/boston-random-forest with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Radhakris55/boston-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
| tags: [sklearn, random-forest, mlops-pipeline, boston] | |
| # Boston Housing Classification Model | |
| Trained automatically using a Random Forest classifier. | |
| The Boston Housing dataset is converted into a binary classification problem. | |
| ## Metrics | |
| - **accuracy**: 0.9118 | |
| - **precision**: 0.9184 | |
| - **recall**: 0.9000 | |
| - **f1**: 0.9091 |