Recommended Hyperparameter Tuning Order
Based on importance analysis, this is the recommended order for tuning hyperparameters.
Tuning Tips
- Focus on high-impact parameters - Parameters at the top of the list have the biggest impact on model performance
- Use a hierarchical approach - Fix less important parameters while tuning the most important ones
- Consider parameter interactions - Some parameters may interact with each other in complex ways
- Use grid search for critical parameters - For the most important parameters, a systematic grid search may be more effective than random search