What are the different hyperparameter tuning techniques?

What are the different hyperparameter tuning techniques?

There are several techniques for hyperparameter tuning, including Grid Search, Random Search, Bayesian Optimization, and more. Would you like a detailed comparison?

Yes, please compare Grid Search and Random Search.

Grid Search exhaustively searches through a specified subset of hyperparameters, while Random Search samples a fixed number of hyperparameter combinations randomly. Grid Search can be more thorough but is often slower.

Which one is generally more effective?

Random Search is often more effective in high-dimensional spaces because it can explore a wider range of values in less time. However, it depends on the specific problem and dataset.

Are there any scenarios where Grid Search is preferred?

Yes, Grid Search is preferred when the hyperparameter space is small and you want to ensure that you explore all combinations thoroughly.