Implement systematic hyperparameter tuning using Optuna, Ray Tune, or custom Bayesian optimization.
Create a comprehensive hyperparameter optimization system for my ML model. Model details: - Model type: [MODEL ARCHITECTURE] - Framework: [PyTorch/TensorFlow/Sklearn] - Key hyperparameters to tune: [LIST PARAMETERS] - Optimization budget: [TIME/TRIALS LIMIT] Requirements: 1. Search space definition with proper distributions 2. Multiple optimization strategies: - Grid search baseline - Random search - Bayesian optimization (TPE, GP) - Hyperband/ASHA for early stopping 3. Cross-validation integration 4. Parallel trial execution 5. Pruning unpromising trials 6. Visualization of optimization history 7. Best hyperparameters export 8. Reproducibility with seed management Use [Optuna/Ray Tune/Weights & Biases] for implementation.
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[MODEL ARCHITECTURE][LIST PARAMETERS]