What is Hyperparameter Tuning in Machine Learning?

In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters are learned. In other words, hyperparameters are points of choice or configuration that allow a machine learning model to be customised for a specific task or dataset. Randomized Search is a method in which random combinations of hyperparameters are selected and used to train a model.