Which of the following is a method for hyperparameter tuning?

Random Search is indeed a method for hyperparameter tuning because it involves selecting random combinations of hyperparameters from a predefined set and evaluating the model's performance using these combinations. This approach allows practitioners to explore the hyperparameter space more broadly than methods that rely on sequential or fixed strategies.

By randomly sampling from the hyperparameter space, Random Search can identify combinations that yield higher performance that might not be found using systematic methods, making it a popular choice when optimizing complex models. The flexibility of Random Search makes it particularly useful when there are many hyperparameters to adjust, as it can efficiently explore various parts of the parameter space without being constrained by a rigid search pattern.

Other methods like grid search, Bayesian optimization, or even optimization algorithms can complement Random Search, but the core premise remains the same: to fine-tune the hyperparameters for improving model performance through strategic exploration.