What are the differences between tuning algorithms?

What are the differences between tuning algorithms?

Tuning algorithms are methods used to optimize hyperparameters in machine learning models. Common types include grid search, random search, and Bayesian optimization. Each has its strengths and weaknesses.

Can you explain how grid search works?

Grid search systematically explores a specified subset of hyperparameters by evaluating all possible combinations. It's exhaustive but can be computationally expensive.

What about random search? How is it different?

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

When should I use Bayesian optimization?

Bayesian optimization is useful when evaluations are expensive. It builds a probabilistic model of the objective function and uses it to select the most promising hyperparameters to evaluate next.

Are there any other tuning methods I should know about?

Yes, there are methods like genetic algorithms and hyperband, which can also be effective depending on your specific use case.