Random Search
Random Search is a hyperparameter optimization technique used in machine learning and optimization problems, where hyperparameters are sampled randomly from a defined search space to find the best configuration for a model or algorithm. It involves evaluating a set of random combinations of hyperparameters and selecting the one that yields the best performance based on a predefined metric, such as accuracy or loss. This method is often used as a baseline or alternative to more systematic approaches like grid search or Bayesian optimization.
Developers should learn and use Random Search when they need a simple, efficient, and scalable way to tune hyperparameters for machine learning models, especially in high-dimensional spaces where grid search becomes computationally expensive. It is particularly useful in scenarios where the relationship between hyperparameters and performance is not well-understood, as it can often find good solutions faster than exhaustive methods, making it ideal for initial exploration or when computational resources are limited.