pejmantheory/bleu-xgboost-classifier-v1.0.0

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

pip install xgboost numpy scikit-learn

Basic Usage

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

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

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:

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:

@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: [email protected]
• 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