What are the different tuning techniques for machine learning models?

What are the different tuning techniques for machine learning models?

There are several tuning techniques, including grid search, random search, and Bayesian optimization. Would you like to know more about each?

Yes, please explain grid search and random search.

Grid search systematically explores a specified subset of hyperparameters, while random search samples random combinations. Grid search can be exhaustive but time-consuming, whereas random search is often faster and can yield good results.

What about Bayesian optimization?

Bayesian optimization uses a probabilistic model to find the best hyperparameters. It is more efficient than grid and random search, especially for expensive evaluations.

Which technique do you recommend for a large dataset?

For large datasets, I recommend Bayesian optimization as it can save time and resources while effectively finding optimal hyperparameters.