Features
hyperparameteroptimizationautomltuningbayesian
Pros and Cons
Ventajas
- + Simple and Pythonic API
- + Advanced search algorithms (TPE, CMA-ES)
- + Pruning of unpromising trials
- + Built-in visualization dashboard
- + Integration with all ML frameworks
- + Parallel and distributed search
Desventajas
- - Requires defining search space
- - Can be computationally expensive
- - Learning curve for advanced options
- - Distributed storage requires setup
- - Results may vary between runs
Use Cases
- Automatic hyperparameter tuning
- Lightweight AutoML
- Network architecture optimization
- Feature selection
- Optimal configuration search
- Reproducible ML experiments