---
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