Which algorithm does Hyperopt use to improve its hyperparameter search efficiency?

Hyperopt utilizes the Tree of Parzen Estimators (TPE) algorithm to enhance the efficiency of its hyperparameter search. TPE is a Bayesian optimization method that models the distribution of good and bad hyperparameter configurations using a non-parametric approach. This allows Hyperopt to make informed decisions about which hyperparameters to explore next, rather than blindly searching the hyperparameter space.

The strength of TPE lies in its ability to focus on regions of the hyperparameter space that are more likely to yield better results based on previous evaluations. By incrementally learning from the performance of different configurations, TPE effectively narrows down its search, leading to improved optimization and faster convergence to optimal hyperparameter values. This method is particularly advantageous in complex models where evaluating all possible hyperparameter combinations would be computationally prohibitive.

The other choices, while they are valid algorithms in their own contexts, do not relate to the hyperparameter optimization strategy employed by Hyperopt.