Скачать или смотреть Implementing RandomizedSearchCV for Efficient Tuning

RandomizedSearchCV is an alternative to GridSearchCV that offers a more efficient approach to hyperparameter tuning. Instead of evaluating all possible combinations of parameters, RandomizedSearchCV samples a fixed number of parameter settings from specified distributions. This method is particularly advantageous when you have a large number of hyperparameters or when exhaustive search is computationally prohibitive. To use RandomizedSearchCV, you define a parameter distribution for each hyperparameter, specifying the range and probability distribution for sampling. You also set the number of iterations to control how many parameter combinations will be evaluated. In practice, RandomizedSearchCV can lead to faster convergence towards optimal hyperparameters with significantly less computational overhead compared to GridSearchCV. It is especially useful when you have limited time or resources. Implementing RandomizedSearchCV in Scikit-learn is straightforward, and it provides a highly flexible and efficient way to explore the hyperparameter space, making it a valuable tool in your machine learning toolkit.