Definition: Hyperparameter tuning is the process of selecting the best values for the hyperparameters of a machine learning model.

Detailed Description:

Hyperparameters are parameters that are not learned from the data but are set before training a model. Tuning hyperparameters can significantly impact the performance of a machine learning model.

Common hyperparameter tuning techniques include:

• Grid search: Trying all possible combinations of hyperparameter values.
• Random search: Randomly sampling hyperparameter values.
• Bayesian optimization: Using Bayesian optimization to efficiently explore the hyperparameter space.

Hyperparameter tuning is often time-consuming and computationally expensive. Automated hyperparameter tuning tools can help streamline this process.

Related Terms:

• Machine Learning
• Model Training
• Hyperparameters
• Optimization

FAQs:

1. What are hyperparameters?

   • Hyperparameters are parameters that are set before training a machine learning model, such as the learning rate, number of layers, or regularization strength. 
     
      

2. Why is hyperparameter tuning important?

   • Hyperparameter tuning can significantly improve the performance of a machine learning model by finding the optimal values for its hyperparameters.

3. What are some challenges in hyperparameter tuning?

   • Hyperparameter tuning can be time-consuming and computationally expensive, especially for complex models.