What are the best practices for tuning algorithm parameters?

What are the best practices for tuning algorithm parameters?

Tuning algorithm parameters is crucial for optimizing performance. Key practices include grid search, random search, and using cross-validation.

Can you explain grid search and how it works?

Grid search involves defining a set of hyperparameters and systematically evaluating all combinations to find the best performance.

What is random search, and how does it differ from grid search?

Random search samples random combinations of hyperparameters, which can be more efficient than grid search, especially in high-dimensional spaces.

How do I know when to stop tuning?

You should stop tuning when improvements become marginal, or when you reach a satisfactory performance level based on validation metrics.