Hyperparameter Tuning

Hyperparameter tuning is the process of optimizing the parameters that govern the training of machine learning models. Unlike model parameters, which are learned during training, hyperparameters are set before the training process begins and can significantly influence model performance. Common techniques for hyperparameter tuning include grid search, random search, and Bayesian optimization, each offering different trade-offs in terms of computational efficiency and effectiveness. This process is crucial in achieving the best possible accuracy and generalization of models in various applications, from image recognition to natural language processing.