automl-framework 0.1.1

A comprehensive, modular framework for automated machine learning with overfitting detection and mitigation

AutoML Framework: Intelligent Machine Learning Automation 🤖🧠

🌟 Project Overview

AutoML Framework is a cutting-edge, comprehensive machine learning library designed to simplify and automate the entire machine learning workflow. Our mission is to democratize machine learning by providing an intelligent, easy-to-use solution that handles complex model selection, optimization, and evaluation.

🚀 Key Differentiators

• Intelligent Model Selection: Automatically tries and evaluates multiple algorithms
• Advanced Overfitting Detection: Sophisticated techniques to prevent model overfitting
• Comprehensive Hyperparameter Optimization: Finds optimal model configurations
• Detailed Performance Reporting: In-depth insights into model performance

📦 Installation

Quick Install

# Install stable version
pip install automl-framework

# Install latest development version
pip install git+https://github.com/nandarizkika/automl-framework.git

Installation Options

# Install with all optional dependencies
pip install automl-framework[all]

# For specific use cases
pip install automl-framework[visualization]  # Visualization tools
pip install automl-framework[tuning]         # Advanced hyperparameter tuning

🧠 Quick Start Examples

Classification Example

from automl import AutoML
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load dataset
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Initialize AutoML
automl = AutoML(problem_type='classification')

# Train and evaluate models
automl.fit(X_train, y_train)
results = automl.evaluate(X_test, y_test)

# Get best model and predictions
best_model = automl.predict(X_test)
leaderboard = automl.get_leaderboard()
print(leaderboard)

Regression Example

from automl import AutoML
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

# Load dataset
X, y = load_boston(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Initialize AutoML for regression
automl = AutoML(problem_type='regression')
automl.fit(X_train, y_train)
results = automl.evaluate(X_test, y_test)

🔧 Advanced Features

Overfitting Detection

# Enable advanced overfitting control
automl.pipeline.set_overfitting_control(
    detection_enabled=True,
    auto_mitigation=True,
    threshold=0.3
)

# Get overfitting assessment
assessment = automl.get_overfitting_assessment()
suggestions = automl.get_improvement_suggestions()

Hyperparameter Tuning

from automl import TuningIntegrator

# Create tuning integrator
tuner = TuningIntegrator(automl)

# Advanced model tuning
summary = tuner.tune_models(
    X_train, y_train,
    search_type='bayesian',
    n_iter=50,
    register_best=True
)

🌈 Key Features

1. Automated Machine Learning

• Automatic algorithm selection
• Intelligent model ranking
• Performance optimization

2. Overfitting Management

• Multi-metric overfitting detection
• Automatic and manual mitigation strategies
• Comprehensive model fit assessment

3. Hyperparameter Optimization

• Grid Search
• Random Search
• Bayesian Optimization
• Hyperopt Integration

4. Comprehensive Reporting

• Detailed performance leaderboards
• Feature importance analysis
• Training logs and insights

🧪 Supported Models

Classification

• Random Forest
• Logistic Regression
• Gradient Boosting
• Support Vector Machines
• Decision Trees
• K-Nearest Neighbors
• Neural Networks

Regression

• Linear Regression
• Ridge Regression
• Random Forest Regressor
• Gradient Boosting Regressor
• Support Vector Regression

📊 Performance Benchmarks

Dataset	Models	Best Accuracy	Training Time	Overfitting Detected
Iris	5	97.8%	2.3s	None
Wine	7	94.4%	4.1s	1 model
Breast Cancer	8	96.5%	8.7s	2 models

🛣️ Roadmap

v0.2.0

• Deep Learning Integration
• Time Series Support
• Advanced Feature Engineering
• Enhanced Visualization

v0.3.0

• Distributed Training
• Model Deployment Tools
• Advanced NLP Support

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for details.

Ways to Contribute

• 🐛 Report Bugs
• 💡 Suggest Features
• 📝 Improve Documentation
• 🔧 Submit Pull Requests

📄 License

MIT License - See LICENSE for details

📞 Support

• 📧 Email: [e-mail]
• 🐞 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

🌟 Star History

---

🚀 Empowering Machine Learning for Everyone 🚀

AutoML Framework: Where Intelligence Meets Automation