---
license: mit
tags:
- machine-learning
- xgboost
- quantum-enhanced
- bleu-js
- classification
- gradient-boosting
datasets:
- custom
metrics:
- accuracy
- f1-score
- roc-auc
model-index:
- name: bleu-xgboost-classifier
  results:
  - task:
      type: classification
    dataset:
      name: Custom Dataset
      type: custom
    metrics:
    - type: accuracy
      value: TBD
    - type: f1-score
      value: TBD
    - type: roc-auc
      value: TBD
---

# Bleu.js XGBoost Classifier

## Model Description

This is an XGBoost classification model from the Bleu.js quantum-enhanced AI platform. The model combines classical gradient boosting with quantum computing capabilities for improved performance and feature extraction.

## Model Details

### Model Type
- **Architecture**: XGBoost Classifier
- **Framework**: XGBoost with quantum-enhanced features
- **Task**: Binary Classification
- **Version**: 1.2.1

### Training Details

#### Training Data
- **Dataset**: Custom training dataset
- **Training Script**: `backend/train_xgboost.py`
- **Data Split**: 80% training, 20% validation

#### Hyperparameters
- `max_depth`: 6
- `learning_rate`: 0.1
- `n_estimators`: 100
- `objective`: binary:logistic
- `random_state`: 42
- `early_stopping_rounds`: 10

#### Preprocessing
- Feature scaling with StandardScaler
- Quantum-enhanced feature extraction (optional)
- Data normalization

### Model Files

- `xgboost_model_latest.pkl`: The trained XGBoost model (latest version)
- `xgboost_model.pkl`: The trained XGBoost model
- `scaler_latest.pkl`: Feature scaler for preprocessing (latest version)
- `scaler.pkl`: Feature scaler for preprocessing

## How to Use

### Installation

```bash
pip install xgboost numpy scikit-learn
```

### Basic Usage

```python
import pickle
import numpy as np
from sklearn.preprocessing import StandardScaler

# Load the model and scaler
with open('xgboost_model_latest.pkl', 'rb') as f:
    model = pickle.load(f)

with open('scaler_latest.pkl', 'rb') as f:
    scaler = pickle.load(f)

# Prepare your data (numpy array with shape: n_samples, n_features)
X = np.array([[feature1, feature2, ...]])

# Scale the features
X_scaled = scaler.transform(X)

# Make predictions
predictions = model.predict(X_scaled)
probabilities = model.predict_proba(X_scaled)

print(f"Predictions: {predictions}")
print(f"Probabilities: {probabilities}")
```

### Using with Bleu.js

```python
from bleujs import BleuJS

# Initialize BleuJS with quantum enhancements
bleu = BleuJS(
    quantum_mode=True,
    model_path="xgboost_model_latest.pkl",
    device="cuda"  # or "cpu"
)

# Process data with quantum features
results = bleu.process(
    input_data=your_data,
    quantum_features=True
)
```

### Download from Hugging Face

```python
from huggingface_hub import hf_hub_download
import pickle

# Download model
model_path = hf_hub_download(
    repo_id="helloblueai/bleu-xgboost-classifier",
    filename="xgboost_model_latest.pkl"
)

scaler_path = hf_hub_download(
    repo_id="helloblueai/bleu-xgboost-classifier",
    filename="scaler_latest.pkl"
)

# Load model
with open(model_path, 'rb') as f:
    model = pickle.load(f)

with open(scaler_path, 'rb') as f:
    scaler = pickle.load(f)
```

## Model Performance

Performance metrics will be updated after evaluation. The model uses:
- Early stopping to prevent overfitting
- Cross-validation for robust evaluation
- Quantum-enhanced features for improved accuracy

## Limitations and Bias

- This model was trained on a specific dataset and may not generalize to other domains
- Performance may vary depending on input data distribution
- Quantum enhancements require compatible hardware for optimal performance
- Model performance depends on data quality and feature engineering

## Training Information

### Training Script
The model is trained using `backend/train_xgboost.py`:

```python
params = {
    "max_depth": 6,
    "learning_rate": 0.1,
    "n_estimators": 100,
    "objective": "binary:logistic",
    "random_state": 42,
}
```

### Evaluation
- Validation set: 20% of training data
- Early stopping: 10 rounds
- Evaluation metric: Log loss (default)

## Citation

If you use this model in your research, please cite:

```bibtex
@software{bleu_js_2024,
  title={Bleu.js: Quantum-Enhanced AI Platform},
  author={HelloblueAI},
  year={2024},
  url={https://github.com/HelloblueAI/Bleu.js},
  version={1.2.1}
}
```

## License

This model is released under the MIT License. See the LICENSE file for more details.

## Contact

For questions or issues, please contact:
- **Email**: support@helloblue.ai
- **GitHub**: https://github.com/HelloblueAI/Bleu.js
- **Organization**: https://huggingface.co/helloblueai

## Acknowledgments

This model is part of the Bleu.js project, which combines classical machine learning with quantum computing capabilities for enhanced performance.

## Related Models

- Bleu.js Quantum Vision Model
- Bleu.js Hybrid Neural Network
- Bleu.js Quantum Feature Extractor