What method involves optimizing hyperparameters through random sampling of parameter combinations?

The method that involves optimizing hyperparameters through random sampling of parameter combinations is called randomized search. Unlike grid search, which evaluates every combination of parameters exhaustively, randomized search selects a random subset of combinations to train and evaluate. This makes it more efficient, especially when dealing with a large number of hyperparameters or when the parameter space is vast.

Randomized search is particularly advantageous because it allows for the exploration of a wider range of values, potentially leading to discovering better-performing models in less time. It leverages the power of randomness to efficiently navigate the hyperparameter space without the exhaustive computational cost associated with grid search. By sampling combinations randomly, this technique can yield strong results without requiring all combinations to be tested, helping practitioners to balance performance with computation time effectively.

In contrast, methods like evolutionary algorithms and Bayesian optimization rely on different strategies, such as iterative improvement or probabilistic modeling, which do not inherently focus on random sampling for hyperparameter tuning in the same manner.