ML or deep learning algorithms take hyperparameters as input prior to training the model. Each algorithm comes with its own set of hyperparameters and some algorithms may have zero hyperparameters.

Hyperparameter tuning is an important step in model building. Each of the ML algorithms comes with some default hyperparameter values that are generally used to build an initial model, unless the practitioner manually overrides the hyperparameters. Setting the right combination of hyperparameters and the right hyperparameter values for the model greatly improves the performance of the model in most cases. Hence, it is strongly recommended that one does hyperparameter tuning as part of ML model building. Searching through the possible universe of hyperparameter values is a very time-consuming task.

The k in k-means clustering and k-nearest neighbors classification, the number of tress and the depth of tress in random forest, and eta in XGBoost are all examples of hyperparameters.

Grid search and Bayesian optimization-based hyperparameter tuning are two popular methods of hyperparameter tuning among practitioners.