Radom Forest Model
This model was trained using Random Forest as part of the AI AutoML Platform.
Features
- Automatic preprocessing
- Missing value handling
- Label encoding
- Feature scaling
- Hyperparameter tuning
- Accuracy optimization
Model Type
Support Vector Machine (SVC)
Library
scikit-learn
Use Cases
- Customer churn prediction
- Medical diagnosis
- Binary classification
- Multi-class classification
Metrics
- Accuracy: XX%
- Precision: XX%
- Recall: XX%
- F1 Score: XX%
Developer
Created by Asma Abid
- Downloads last month
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from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Asma-Abid/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