How does Hyperopt differ from grid search in hyperparameter tuning?

Hyperopt is designed for more efficient hyperparameter tuning by specifying ranges for hyperparameters rather than testing individual values. This approach allows Hyperopt to explore a larger search space without having to evaluate every possible configuration, as is done in grid search. Grid search systematically goes through all specified hyperparameter combinations, which can become computationally expensive and inefficient, especially in scenarios with high-dimensional hyperparameter spaces.

By contrast, Hyperopt leverages probabilistic models to choose hyperparameters based on prior observations. This means it can focus on promising areas of the search space, possibly leading to better models with fewer evaluations. The ability to define ranges for hyperparameters means Hyperopt can dynamically discover which values yield better performance without needing to explicitly test every single one within those ranges.

In essence, the key difference lies in Hyperopt’s capability to optimize over statistical ranges, leading to more informed and efficient tuning compared to the exhaustive nature of grid search.