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
File size: 334 Bytes
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tags: [sklearn, random-forest, mlops-pipeline, boston]
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# 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 |