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---
library_name: sklearn
tags:
- sklearn
- scikit-learn
- classification
- wine
- random-forest
---
# Wine Classification Model
A RandomForestClassifier model trained on the UCI Wine dataset for wine classification.
## Model Details
- **Model Type**: RandomForestClassifier
- **Dataset**: UCI Wine Dataset
- **Number of Features**: 13
- **Number of Classes**: 3
- **Classes**: class_0, class_1, class_2
## Model Parameters
- `n_estimators`: 100
- `max_depth`: 6
- `random_state`: 42
## Usage
### Using Hugging Face Hub
```python
from huggingface_hub import hf_hub_download
import joblib
# Download and load the model
model_path = hf_hub_download(repo_id="alirisheh/test1", filename="model.joblib")
model = joblib.load(model_path)
# Make predictions
predictions = model.predict(X_test)
```
### Using the Hugging Face Inference API
You can also use this model with the Hugging Face Inference API once it's deployed.
## Training
The model was trained on the scikit-learn wine dataset with an 80/20 train/test split.
## Evaluation
The model achieves high accuracy on the test set. See `model_metadata.json` for detailed metrics.
## Files
- `model.joblib`: The trained scikit-learn model
- `model_metadata.json`: Model metadata and training information
- `sample_input.json`: Sample input for testing
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