Instructions to use IlamathiV/breast-cancer-rf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use IlamathiV/breast-cancer-rf with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("IlamathiV/breast-cancer-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
Breast Cancer Classifier (RandomForest)
Trained automatically via GitHub Actions CI/CD. Deployed only when the
evaluation accuracy meets the quality gate defined in params.yaml.
Metrics
| Metric | Value |
|---|---|
| Accuracy | 0.9474 |
| Precision | 0.9583 |
| Recall | 0.9583 |
| F1 | 0.9583 |
| ROC-AUC | 0.9940 |
Usage
import joblib
from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id="IlamathiV/breast-cancer-rf", filename="model.pkl")
model = joblib.load(path)
predictions = model.predict(X)
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