Term
Hyperparameter Search (ˌhaɪ-pər-ˈpær-ə-miː-tər sɜːrʧ)
Definition
Hyperparameter Search is a method used in AI to find the most effective settings for a model by experimenting with different combinations of parameters. This approach optimizes the performance of machine learning models.
Where you’ll find it
This feature typically appears in the settings or advanced options sections of AI and machine learning platforms. Access to Hyperparameter Search may vary based on the subscription plan or software version in use.
Common use cases
- Improving Accuracy: If your model isn't performing as well as expected, tweaking the hyperparameters could lead to better results.
- Experimentation: It helps to test how different settings impact the performance of your models.
- Efficiency: Identifying the best parameters can make the model more accurate, faster, and more efficient to run.
Things to watch out for
- Resource Intensive: Searching for the best model parameters can be computationally expensive and time-consuming.
- Knowledge Required: Understanding which hyperparameters are most impactful requires some knowledge of how they relate to the model's performance.
- Overfitting: Be cautious of choosing hyperparameters that perform exceptionally well on the training data but poorly on unseen data.
Related terms
- Machine Learning Models
- Optimization Algorithms
- Model Tuning
- Training Data
- Performance Metrics
Pixelhaze Tip: Start with a broad search using widely different hyperparameter values to see the range of model behaviors. Then refine your search around the best-performing settings. This two-step approach can save time and resources.