🇬🇧 English | 🇰🇷 한국어

🚀 Hyperparameter Optimization Tutorial

Practical comparison of 5 hyperparameter optimization algorithms for machine learning

🇰🇷 한국어 | 🇬🇧 English | 🎯 Quick Start | 📊 Results

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⚡ Key Results

Typical Performance Pattern (Diabetes Dataset: 442 samples, 10 features, 50 iterations)

Method	Typical Improvement	Speed	Best For
TPE (Hyperopt)	~27% ⭐⭐	Fastest ⚡	Best overall performance
Random Search	~26% ⭐	Fast	Quick prototyping, reliable
Optuna (TPE+Pruning)	~26% ⭐	Fast	Production systems
Bayesian Optimization	~26% ⭐	Moderate	Critical performance needs
Grid Search	~22%	Slow	Small search spaces
Baseline (default)	0%	-	Reference point

💡 Important Note: Actual results vary based on random_state, data split, and environment. All methods typically improve baseline by 20-27%. Run the notebook to see results on your machine.

⚡ Key Insight: TPE (Hyperopt) achieved the highest improvement (+27.12%), closely followed by Random Search (+26.33%) and Optuna (+26.02%). Modern Bayesian methods consistently outperform Grid Search with better efficiency.

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🎓 What You'll Learn

📚 Five Optimization Algorithms

1. Grid Search - Exhaustive search through all parameter combinations
2. Random Search - Random sampling from parameter distributions
3. Optuna - Modern TPE with pruning (replaces deprecated HyperBand)
4. Bayesian Optimization - Probabilistic model-based optimization
5. TPE (Hyperopt) - Tree-structured Parzen Estimator

🎯 Learning Outcomes

• Understand strengths and weaknesses of each algorithm
• Know which method to choose for different scenarios
• Implement optimization in real projects with working code
• Compare results with statistical rigor
• Reduce hyperparameter tuning time significantly

🗺️ Concept Mind Map

📌 Click to view Hyperparameter Optimization Concept Mind Map

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🚀 Quick Start

Installation

git clone https://github.com/hyeonsangjeon/Hyperparameters-Optimization.git
cd Hyperparameters-Optimization
pip install -r requirements.txt

Run Tutorial

Interactive Notebook (Recommended)

jupyter notebook HyperParameterInspect.ipynb        # Korean
jupyter notebook HyperParameterInspect_EN.ipynb     # English

Automated Benchmark

python benchmark_hpo_algorithms.py

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📊 Algorithm Comparison

Selection Guide

Your Scenario	Recommended	Why
Quick prototyping	Random Search	Fast setup, decent results
Production deployment	Optuna	Modern, pruning, actively maintained
Best performance needed	Bayesian Optimization	Superior results, worth extra time
Limited time budget	TPE (Hyperopt)	Best speed/quality tradeoff
Small discrete space	Grid Search	Guarantees finding optimum
Research paper	Bayesian + TPE	Multiple strong baselines

Algorithm Details

Algorithm	How It Works	Strengths	Limitations
Grid Search	Exhaustive evaluation of all combinations	Complete coverage, reproducible	Exponential complexity
Random Search	Random sampling from distributions	Fast, handles continuous params	No learning between trials
Optuna	TPE with automatic pruning	Modern, efficient, production-ready	Requires setup
Bayesian Optimization	Gaussian process model of objective	Intelligent search, best results	Slower initial phase
TPE	Tree-structured Parzen estimators	Fast convergence, proven reliability	Fewer features than Optuna

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🏆 Benchmark Details

Experimental Setup

• Dataset: Sklearn Diabetes (442 samples, 10 features)
• Model: LightGBM Regressor
• Iterations: 50 trials per method
• Validation: 2-fold cross-validation
• Metric: Mean Squared Error (lower is better)

Performance Characteristics

Algorithm	Speed	Consistency	Typical Improvement
TPE (Hyperopt)	⚡⚡⚡ Fastest	High	25-35%
Optuna	⚡⚡⚡ Very Fast	High	20-30%
Random Search	⚡⚡ Fast	Medium	20-30%
Bayesian Opt	⚡ Moderate	High	20-30%
Grid Search	❌ Slow	Very High	15-25%

⚠️ Note: Values shown are from recent benchmark run. Absolute MSE values vary by environment and random_state, but the ranking and relative performance are consistent across runs.

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📁 Project Structure

Hyperparameters-Optimization/
├── HyperParameterInspect.ipynb           # Korean tutorial notebook
├── HyperParameterInspect_EN.ipynb        # English tutorial notebook
├── benchmark_hpo_algorithms.py           # Automated benchmark script
├── requirements.txt                      # Python dependencies
├── README.md                             # This file
├── pic/                                  # Images and plots
└── doc/                                  # Additional documentation

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🔧 Requirements

Core Dependencies

• Python 3.8+
• numpy, pandas, scikit-learn, lightgbm

Optimization Libraries

• optuna >= 3.0.0 (Modern HPO with pruning)
• hyperopt >= 0.2.7 (TPE algorithm)
• scikit-optimize >= 0.9.0 (Bayesian optimization)

Visualization

• matplotlib, jupyter

⚠️ Important: This project uses Optuna instead of the deprecated scikit-hyperband library due to compatibility issues with modern scikit-learn versions.

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📚 References

Key Papers

• Random Search: Bergstra & Bengio, JMLR 2012
• TPE: Bergstra et al., NIPS 2011
• Bayesian Optimization: Snoek et al., 2012
• HyperBand: Li et al., ICLR 2018

Presentations & Media

• 🎤 Hyeonsang Jeon, "Expert Lecture: Hyperparameter Optimization in AI Modeling", ITDAILY, 2022. Article

• 🎤 Hyeonsang Jeon, "Case Study: AutoDL with Hyperparameter Optimization in Deep Learning Platforms", AI Innovation 2020, The Electronic Times, 2020. Video

• 📰 Featured in ComWorld

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🤝 Contributing

Contributions welcome! Ways to help:

• 🐛 Report bugs or issues
• 💡 Suggest new features or algorithms
• 📝 Improve documentation
• 🌍 Translate to other languages
• 🔬 Add optimization methods

Development Setup

git clone https://github.com/YOUR_USERNAME/Hyperparameters-Optimization.git
cd Hyperparameters-Optimization
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python benchmark_hpo_algorithms.py

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License

MIT License - see LICENSE file for details.

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👤 Author

Hyeonsang Jeon
GitHub: @hyeonsangjeon

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🙏 Acknowledgments

Special thanks to:

• Optuna - Modern HPO framework
• Hyperopt - TPE implementation
• scikit-optimize - Bayesian optimization
• LightGBM - Fast gradient boosting

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🔗 Related Projects

• Optuna - Next-generation HPO framework
• Hyperopt - Distributed HPO library
• scikit-optimize - Bayesian optimization
• Ray Tune - Scalable distributed tuning

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This tutorial is actively maintained and regularly updated with new techniques.

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