Hyperparameter-Optimierung (EN)

Hyperparameter-Optimierung

Hyperparameter optimization is a systematic process for finding the best hyperparameters for a machine learning model. Hyperparameters are configurable parameters that are not directly learned from the data but must be set before training. This process can be performed through methods such as Grid Search, Random Search, or advanced techniques like Bayesian Optimization. Careful optimization of hyperparameters can significantly improve a model's performance and is a crucial step in the ML pipeline.

Process of Hyperparameter Optimization

flowchart TD     A[Start] --> B[Define hyperparameter space]     B --> C[Select optimization strategy]     C --> D{Grid Search/Random Search/Bayesian Optimization}     D --> E[Train model with current hyperparameters]     E --> F[Evaluate model performance]     F --> G{Result satisfactory?}     G -->|No| H[Adjust hyperparameters]     H --> E     G -->|Yes| I[Optimized hyperparameters found]     I --> J[End]