Description: Hyperparameter tuning is the process of searching for the optimal hyperparameters for a machine learning model. Hyperparameters are configurations set before model training that affect performance and generalization ability. Unlike model parameters, which are adjusted during training, hyperparameters must be defined by the user. This process is crucial, as inadequate hyperparameter selection can lead to a model that does not fit the data well, resulting in suboptimal performance. There are various techniques for hyperparameter tuning, including random search, grid search, and more advanced methods like Bayesian optimization. Hyperparameter tuning has become an essential component in the field of AutoML (automated machine learning), where the goal is to simplify and optimize the modeling process so that even users without programming experience can build effective models. Automating this process not only saves time but can also uncover configurations that a human might overlook, thereby improving the accuracy and robustness of the final model.
