Random Search

Random Search is a hyperparameter optimization technique used in machine learning to find the best set of hyperparameters for a model. Unlike grid search, which tests all combinations of parameters, random search randomly samples from the parameter space, allowing it to explore a broader area in less time. This method is particularly effective when dealing with high-dimensional spaces, as it can lead to better results with fewer evaluations. Common use cases include optimizing models in classification, regression, and neural network training, where selecting the right hyperparameters can significantly impact performance.