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pejmantheory/
bleu-xgboost-classifier

Name: bleu-xgboost-classifier
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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

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

pejmantheory/bleu-xgboost-classifier

Bleu.js XGBoost Classifier

Model Description

Model Details

Model Type

Training Details

Training Data

Hyperparameters

Preprocessing

Model Files

How to Use

Installation

Basic Usage

Using with Bleu.js

Download from Hugging Face

Model Performance

Limitations and Bias

Training Information

Training Script

Evaluation

Citation

License

Contact

Acknowledgments

Related Models

pejmantheory/
bleu-xgboost-classifier