|
|
--- |
|
|
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 |
|
|
|
