Hyperparameter Tuning

Hyperparameter tuning refers to the process of optimizing the parameters of a machine learning algorithm that are not learned from the data itself but are set before the training process. These parameters, known as hyperparameters, control the learning process and can significantly impact the model's performance. Examples of hyperparameters include the learning rate, the number of hidden layers in a neural network, and the batch size.The tuning process involves selecting the best set of hyperparameters through various methods, such as grid search, random search, or more advanced techniques like Bayesian optimization. The goal is to find the configuration that yields the highest performance on a validation dataset, balancing the trade-off between model complexity and generalization ability on unseen data. Hyperparameter tuning is a critical step in the development of effective machine learning models, as the wrong set of hyperparameters can lead to overfitting, underfitting, or suboptimal performance.