Random search is a technique used in machine learning and optimization that involves selecting random combinations of hyperparameters in order to evaluate their effectiveness in improving a model's performance. This method contrasts with more systematic approaches like grid search, allowing for exploration of a broader range of hyperparameter settings with less computational effort. Random search is particularly useful in both supervised and unsupervised learning, where finding the optimal configuration can significantly influence the results.
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