Random Search
Random search is a hyperparameter optimization technique in machine learning that involves randomly selecting combinations of hyperparameters from a defined search space and evaluating their performance on a given task. This method is often used as a baseline for more advanced optimization techniques such as grid search, Bayesian optimization, and evolutionary algorithms. Random search is particularly useful when the search space is large and the relationship between hyperparameters and performance is complex or unknown. By randomly sampling from the search space, random search can efficiently explore a wide range of hyperparameter combinations and identify promising regions of the search space. This can lead to significant improvements in model performance compared to using default hyperparameters or manually tuning hyperparameters. However, random search does not guarantee finding the optimal set of hyperparameters and may require a large number of trials to achieve good results.
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