Definition
Hyperparameter tuning involves evaluating different sets of hyperparameters to find the configuration that yields the best model accuracy or lowest error rate. Common strategies include grid search, which exhaustively checks all combinations, and random search, which samples randomly. More advanced techniques use Bayesian optimization to intelligently select promising configurations based on previous results. This process is computationally expensive but essential for maximizing the potential of machine learning models.
Summary
The process of systematically searching for the best combination of hyperparameters to optimize model performance.
Key Concepts
- Grid Search
- Random Search
- Cross-Validation
- Performance Metrics
Use Cases
- Optimizing deep learning models
- Improving classifier accuracy
- Reducing model bias and variance