File size: 2,420 Bytes
14a6827 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | # 🍷 Wine Type Classifier
A **GradientBoostingClassifier** that predicts whether a wine is **red** or **white** based on its chemical properties.
## Model Details
- **Model type**: scikit-learn GradientBoostingClassifier
- **Task**: Binary classification (red vs white wine)
- **Dataset**: [mstz/wine](https://huggingface.co/datasets/mstz/wine)
- **Training samples**: 5,197
- **Test samples**: 1,300
## Performance
| Metric | Score |
|--------|-------|
| **Test Accuracy** | 99.23% |
| **Test F1 Score** | 99.49% |
| **Train Accuracy** | 100.0% |
### Per-class Performance (Test Set)
| Class | Precision | Recall | F1 |
|-------|-----------|--------|-----|
| Red Wine | 0.98 | 0.99 | 0.98 |
| White Wine | 1.00 | 0.99 | 0.99 |
## Features
The model uses 12 chemical properties as input features:
| Feature | Importance |
|---------|-----------|
| `total_sulfur_dioxide` | 58.06% |
| `chlorides` | 31.25% |
| `density` | 3.40% |
| `volatile_acidity` | 2.27% |
| `sulphates` | 1.38% |
| `fixed_acidity` | 0.85% |
| `residual_sugar` | 0.81% |
| `free_sulfur_dioxide` | 0.76% |
| `citric_acid` | 0.57% |
| `pH` | 0.34% |
| `alcohol` | 0.22% |
| `quality` | 0.10% |
## Usage
```python
import pickle
import numpy as np
from huggingface_hub import hf_hub_download
# Download and load model
model_path = hf_hub_download("victor/wine-type-classifier", "model.pkl")
with open(model_path, "rb") as f:
model = pickle.load(f)
# Labels: 0 = Red Wine, 1 = White Wine
labels = {0: "Red Wine", 1: "White Wine"}
# Input features (in order):
# fixed_acidity, volatile_acidity, citric_acid, residual_sugar,
# chlorides, free_sulfur_dioxide, total_sulfur_dioxide,
# density, pH, sulphates, alcohol, quality
# Example: predict a red wine
sample = np.array([[7.4, 0.7, 0.0, 1.9, 0.076, 11.0, 34.0, 0.9978, 3.51, 0.56, 9.4, 5]])
prediction = model.predict(sample)[0]
probabilities = model.predict_proba(sample)[0]
print(f"Prediction: {labels[prediction]}")
print(f"Confidence: {max(probabilities):.2%}")
```
## Label Mapping
> ⚠️ **Note**: The `is_red` column in the source dataset is inverted relative to its name:
> - `is_red=0` → **Red Wine** (1,599 samples; high volatile acidity, low sulfur dioxide)
> - `is_red=1` → **White Wine** (4,898 samples; low volatile acidity, high sulfur dioxide)
## Training
```bash
pip install scikit-learn datasets huggingface_hub
python train_wine.py
```
## License
MIT
|