What is tuning in machine learning?
Model tuning bridges raw capability and real-world performance.
How It Works:
Tuning adjusts hyperparameters (like learning rate, batch size, regularization strength) to find the best combination that maximizes model performance.
Key Benefits:
- Unlocks higher accuracy
- Controls overfitting and underfitting
- Optimizes resource usage
Real-World Use Cases:
- Grid or random search for small models
- Bayesian optimization for large training runs
FAQs
How many parameters to tune?
Does tuning take long?
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